Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory4.5 KiB

Variable types

Text18
Numeric1
Categorical36

Alerts

Authority_Present is highly overall correlated with Clarity_and_Conciseness_Value and 35 other fieldsHigh correlation
Clarity_and_Conciseness_Value is highly overall correlated with Authority_Present and 32 other fieldsHigh correlation
Contains_Colon is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Contains_Exclamation_Mark is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Contains_Hyphen is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Contains_Numbers is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Contains_Question_Mark is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Contains_Quotes is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Curiosity_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Economic_Benefit_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Economic_Benefit_Words is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Emphatic_Capitalization_Usage is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Ends_With_Question_Mark is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Exclusivity_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Exclusivity_Words is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Fear_Concern_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Hope_Optimism_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Indignation_Controversy_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Length_General_Assessment is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Main_Category is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Main_Classification is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
National_Relevance_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Originality_and_Differentiation_Value is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Personal_Identification_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Prohibition_Restriction_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Prohibition_Restriction_Words is highly overall correlated with Authority_Present and 26 other fieldsHigh correlation
Recognized_Brand_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Relevance_and_Timeliness_Value is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Solution_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Solution_Words is highly overall correlated with Authority_Present and 26 other fieldsHigh correlation
Starts_With_Number is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Strategic_Keyword_Usage_Value is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Subcategory_2 is highly overall correlated with Authority_Present and 26 other fieldsHigh correlation
Surprise_Awe_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Temporal_Urgency_Present is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Temporal_Urgency_Words is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Visibility is highly overall correlated with Authority_Present and 35 other fieldsHigh correlation
Subcategory_2 is highly imbalanced (57.1%) Imbalance
Clarity_and_Conciseness_Value is highly imbalanced (88.3%) Imbalance
Relevance_and_Timeliness_Value is highly imbalanced (80.1%) Imbalance
Strategic_Keyword_Usage_Value is highly imbalanced (83.4%) Imbalance
Contains_Quotes is highly imbalanced (56.8%) Imbalance
Contains_Question_Mark is highly imbalanced (77.1%) Imbalance
Contains_Colon is highly imbalanced (56.8%) Imbalance
Contains_Exclamation_Mark is highly imbalanced (87.8%) Imbalance
Starts_With_Number is highly imbalanced (69.3%) Imbalance
Ends_With_Question_Mark is highly imbalanced (79.0%) Imbalance
Length_General_Assessment is highly imbalanced (71.7%) Imbalance
Emphatic_Capitalization_Usage is highly imbalanced (85.3%) Imbalance
Temporal_Urgency_Present is highly imbalanced (54.1%) Imbalance
Temporal_Urgency_Words is highly imbalanced (71.6%) Imbalance
Exclusivity_Present is highly imbalanced (82.2%) Imbalance
Exclusivity_Words is highly imbalanced (88.7%) Imbalance
Solution_Present is highly imbalanced (51.1%) Imbalance
Solution_Words is highly imbalanced (69.5%) Imbalance
Economic_Benefit_Present is highly imbalanced (55.7%) Imbalance
Economic_Benefit_Words is highly imbalanced (73.2%) Imbalance
Prohibition_Restriction_Present is highly imbalanced (62.9%) Imbalance
Prohibition_Restriction_Words is highly imbalanced (78.3%) Imbalance
Indignation_Controversy_Present is highly imbalanced (50.1%) Imbalance
Title has unique values Unique
Visibility has unique values Unique

Reproduction

Analysis started2025-07-02 14:31:14.805314
Analysis finished2025-07-02 14:31:25.021576
Duration10.22 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Title
Text

Unique 

Distinct300
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size52.7 KiB
2025-07-02T14:31:25.386905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length239
Median length120.5
Mean length83.82
Min length20

Characters and Unicode

Total characters25146
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300 ?
Unique (%)100.0%

Sample

1st rowFord is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd rowNew U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd rowCadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th rowThe DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th rowMcDonald's Removing 1 Breakfast Menu Item for Good on July 2
ValueCountFrequency (%)
to 126
 
3.0%
the 101
 
2.4%
in 79
 
1.9%
for 67
 
1.6%
and 61
 
1.5%
new 54
 
1.3%
of 54
 
1.3%
a 52
 
1.2%
with 34
 
0.8%
is 30
 
0.7%
Other values (1812) 3516
84.2%
2025-07-02T14:31:26.157232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3874
15.4%
e 2311
 
9.2%
a 1532
 
6.1%
o 1485
 
5.9%
t 1449
 
5.8%
r 1395
 
5.5%
n 1377
 
5.5%
i 1343
 
5.3%
s 1231
 
4.9%
l 877
 
3.5%
Other values (76) 8272
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3874
15.4%
e 2311
 
9.2%
a 1532
 
6.1%
o 1485
 
5.9%
t 1449
 
5.8%
r 1395
 
5.5%
n 1377
 
5.5%
i 1343
 
5.3%
s 1231
 
4.9%
l 877
 
3.5%
Other values (76) 8272
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3874
15.4%
e 2311
 
9.2%
a 1532
 
6.1%
o 1485
 
5.9%
t 1449
 
5.8%
r 1395
 
5.5%
n 1377
 
5.5%
i 1343
 
5.3%
s 1231
 
4.9%
l 877
 
3.5%
Other values (76) 8272
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3874
15.4%
e 2311
 
9.2%
a 1532
 
6.1%
o 1485
 
5.9%
t 1449
 
5.8%
r 1395
 
5.5%
n 1377
 
5.5%
i 1343
 
5.3%
s 1231
 
4.9%
l 877
 
3.5%
Other values (76) 8272
32.9%

Visibility
Real number (ℝ)

High correlation  Unique 

Distinct300
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4500275.1
Minimum1993759
Maximum22572066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-07-02T14:31:26.289481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1993759
5-th percentile2069848.6
Q12471328
median3352569
Q35180321.2
95-th percentile10804184
Maximum22572066
Range20578307
Interquartile range (IQR)2708993.2

Descriptive statistics

Standard deviation3231247.7
Coefficient of variation (CV)0.71801113
Kurtosis8.4699825
Mean4500275.1
Median Absolute Deviation (MAD)1042408.5
Skewness2.5928748
Sum1.3500825 × 109
Variance1.0440961 × 1013
MonotonicityStrictly decreasing
2025-07-02T14:31:26.428794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1993759 1
 
0.3%
22572066 1
 
0.3%
21331409 1
 
0.3%
19344936 1
 
0.3%
18797641 1
 
0.3%
16353543 1
 
0.3%
15318643 1
 
0.3%
14627061 1
 
0.3%
13507301 1
 
0.3%
13339952 1
 
0.3%
Other values (290) 290
96.7%
ValueCountFrequency (%)
1993759 1
0.3%
1994857 1
0.3%
2002469 1
0.3%
2014724 1
0.3%
2018517 1
0.3%
2019272 1
0.3%
2022447 1
0.3%
2026378 1
0.3%
2032696 1
0.3%
2036769 1
0.3%
ValueCountFrequency (%)
22572066 1
0.3%
21331409 1
0.3%
19344936 1
0.3%
18797641 1
0.3%
16353543 1
0.3%
15318643 1
0.3%
14627061 1
0.3%
13507301 1
0.3%
13339952 1
0.3%
13255807 1
0.3%
Distinct296
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size51.2 KiB
2025-07-02T14:31:26.840433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length239
Median length120
Mean length81.1
Min length0

Characters and Unicode

Total characters24330
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)98.3%

Sample

1st rowFord is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd rowNew U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd rowCadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th rowThe DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th rowMcDonald's Removing 1 Breakfast Menu Item for Good on July 2
ValueCountFrequency (%)
to 126
 
3.1%
the 99
 
2.4%
in 77
 
1.9%
for 66
 
1.6%
and 58
 
1.4%
of 54
 
1.3%
new 52
 
1.3%
a 50
 
1.2%
with 31
 
0.8%
is 30
 
0.7%
Other values (1768) 3408
84.1%
2025-07-02T14:31:27.418738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3756
15.4%
e 2236
 
9.2%
a 1477
 
6.1%
o 1448
 
6.0%
t 1408
 
5.8%
r 1349
 
5.5%
n 1337
 
5.5%
i 1302
 
5.4%
s 1194
 
4.9%
l 844
 
3.5%
Other values (75) 7979
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3756
15.4%
e 2236
 
9.2%
a 1477
 
6.1%
o 1448
 
6.0%
t 1408
 
5.8%
r 1349
 
5.5%
n 1337
 
5.5%
i 1302
 
5.4%
s 1194
 
4.9%
l 844
 
3.5%
Other values (75) 7979
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3756
15.4%
e 2236
 
9.2%
a 1477
 
6.1%
o 1448
 
6.0%
t 1408
 
5.8%
r 1349
 
5.5%
n 1337
 
5.5%
i 1302
 
5.4%
s 1194
 
4.9%
l 844
 
3.5%
Other values (75) 7979
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3756
15.4%
e 2236
 
9.2%
a 1477
 
6.1%
o 1448
 
6.0%
t 1408
 
5.8%
r 1349
 
5.5%
n 1337
 
5.5%
i 1302
 
5.4%
s 1194
 
4.9%
l 844
 
3.5%
Other values (75) 7979
32.8%

Main_Category
Categorical

High correlation 

Distinct15
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size22.0 KiB
Finance_and_Business
65 
News_and_Current_Events
65 
Entertainment_and_Culture
30 
Gastronomy
23 
Health_and_Wellness
19 
Other values (10)
98 

Length

Max length26
Median length23
Mean length17.57
Min length0

Characters and Unicode

Total characters5271
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowFinance_and_Business
2nd rowNews_and_Current_Events
3rd rowNews_and_Current_Events
4th rowNews_and_Current_Events
5th rowGastronomy

Common Values

ValueCountFrequency (%)
Finance_and_Business 65
21.7%
News_and_Current_Events 65
21.7%
Entertainment_and_Culture 30
10.0%
Gastronomy 23
 
7.7%
Health_and_Wellness 19
 
6.3%
Science 16
 
5.3%
Public_Safety 16
 
5.3%
Travel 14
 
4.7%
Education_and_Guides 12
 
4.0%
Sports 12
 
4.0%
Other values (5) 28
9.3%

Length

2025-07-02T14:31:27.551577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
finance_and_business 65
22.0%
news_and_current_events 65
22.0%
entertainment_and_culture 30
10.2%
gastronomy 23
 
7.8%
health_and_wellness 19
 
6.4%
science 16
 
5.4%
public_safety 16
 
5.4%
travel 14
 
4.7%
education_and_guides 12
 
4.1%
sports 12
 
4.1%
Other values (4) 23
 
7.8%

Most occurring characters

ValueCountFrequency (%)
n 706
13.4%
e 595
11.3%
_ 497
 
9.4%
s 442
 
8.4%
a 395
 
7.5%
t 349
 
6.6%
i 255
 
4.8%
r 246
 
4.7%
u 237
 
4.5%
d 232
 
4.4%
Other values (25) 1317
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 706
13.4%
e 595
11.3%
_ 497
 
9.4%
s 442
 
8.4%
a 395
 
7.5%
t 349
 
6.6%
i 255
 
4.8%
r 246
 
4.7%
u 237
 
4.5%
d 232
 
4.4%
Other values (25) 1317
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 706
13.4%
e 595
11.3%
_ 497
 
9.4%
s 442
 
8.4%
a 395
 
7.5%
t 349
 
6.6%
i 255
 
4.8%
r 246
 
4.7%
u 237
 
4.5%
d 232
 
4.4%
Other values (25) 1317
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 706
13.4%
e 595
11.3%
_ 497
 
9.4%
s 442
 
8.4%
a 395
 
7.5%
t 349
 
6.6%
i 255
 
4.8%
r 246
 
4.7%
u 237
 
4.5%
d 232
 
4.4%
Other values (25) 1317
25.0%
Distinct72
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size21.7 KiB
2025-07-02T14:31:27.785407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length22
Mean length16.496667
Min length0

Characters and Unicode

Total characters4949
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)9.7%

Sample

1st rowCompanies & Entrepreneurship
2nd rowPolitics
3rd rowPolitics
4th rowPolitics
5th rowRestaurants & Chefs
ValueCountFrequency (%)
101
 
18.2%
companies 27
 
4.9%
entrepreneurship 27
 
4.9%
politics 19
 
3.4%
celebrities 16
 
2.9%
influencers 16
 
2.9%
prevention 13
 
2.3%
alerts 12
 
2.2%
recipes 12
 
2.2%
companies_and_entrepreneurship 12
 
2.2%
Other values (92) 299
54.0%
2025-07-02T14:31:28.189242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 540
 
10.9%
i 457
 
9.2%
n 454
 
9.2%
r 346
 
7.0%
s 325
 
6.6%
t 298
 
6.0%
a 278
 
5.6%
259
 
5.2%
o 228
 
4.6%
l 205
 
4.1%
Other values (40) 1559
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 540
 
10.9%
i 457
 
9.2%
n 454
 
9.2%
r 346
 
7.0%
s 325
 
6.6%
t 298
 
6.0%
a 278
 
5.6%
259
 
5.2%
o 228
 
4.6%
l 205
 
4.1%
Other values (40) 1559
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 540
 
10.9%
i 457
 
9.2%
n 454
 
9.2%
r 346
 
7.0%
s 325
 
6.6%
t 298
 
6.0%
a 278
 
5.6%
259
 
5.2%
o 228
 
4.6%
l 205
 
4.1%
Other values (40) 1559
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 540
 
10.9%
i 457
 
9.2%
n 454
 
9.2%
r 346
 
7.0%
s 325
 
6.6%
t 298
 
6.0%
a 278
 
5.6%
259
 
5.2%
o 228
 
4.6%
l 205
 
4.1%
Other values (40) 1559
31.5%

Subcategory_2
Categorical

High correlation  Imbalance 

Distinct34
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
N/A
211 
Government
 
16
Tips
 
11
Advances
 
7
National
 
6
Other values (29)
49 

Length

Max length25
Median length3
Mean length5.0233333
Min length0

Characters and Unicode

Total characters1507
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)5.7%

Sample

1st rowN/A
2nd rowGovernment
3rd rowGovernment
4th rowGovernment
5th rowN/A

Common Values

ValueCountFrequency (%)
N/A 211
70.3%
Government 16
 
5.3%
Tips 11
 
3.7%
Advances 7
 
2.3%
National 6
 
2.0%
5
 
1.7%
Desserts 4
 
1.3%
Main Courses 3
 
1.0%
Alerts & Prevention 3
 
1.0%
Analysis 3
 
1.0%
Other values (24) 31
 
10.3%

Length

2025-07-02T14:31:28.317903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n/a 211
66.4%
government 16
 
5.0%
tips 11
 
3.5%
advances 7
 
2.2%
7
 
2.2%
national 6
 
1.9%
prevention 5
 
1.6%
desserts 4
 
1.3%
main 3
 
0.9%
alerts 3
 
0.9%
Other values (33) 45
 
14.2%

Most occurring characters

ValueCountFrequency (%)
A 224
14.9%
N 217
14.4%
/ 212
14.1%
e 123
 
8.2%
n 93
 
6.2%
s 82
 
5.4%
i 63
 
4.2%
t 58
 
3.8%
r 55
 
3.6%
o 45
 
3.0%
Other values (32) 335
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 224
14.9%
N 217
14.4%
/ 212
14.1%
e 123
 
8.2%
n 93
 
6.2%
s 82
 
5.4%
i 63
 
4.2%
t 58
 
3.8%
r 55
 
3.6%
o 45
 
3.0%
Other values (32) 335
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 224
14.9%
N 217
14.4%
/ 212
14.1%
e 123
 
8.2%
n 93
 
6.2%
s 82
 
5.4%
i 63
 
4.2%
t 58
 
3.8%
r 55
 
3.6%
o 45
 
3.0%
Other values (32) 335
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 224
14.9%
N 217
14.4%
/ 212
14.1%
e 123
 
8.2%
n 93
 
6.2%
s 82
 
5.4%
i 63
 
4.2%
t 58
 
3.8%
r 55
 
3.6%
o 45
 
3.0%
Other values (32) 335
22.2%

Clarity_and_Conciseness_Value
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
High
291 
 
5
Medium
 
3
Low
 
1

Length

Max length6
Median length4
Mean length3.95
Min length0

Characters and Unicode

Total characters1185
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 291
97.0%
5
 
1.7%
Medium 3
 
1.0%
Low 1
 
0.3%

Length

2025-07-02T14:31:28.424595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:28.519369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 291
98.6%
medium 3
 
1.0%
low 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 294
24.8%
H 291
24.6%
g 291
24.6%
h 291
24.6%
M 3
 
0.3%
e 3
 
0.3%
d 3
 
0.3%
u 3
 
0.3%
m 3
 
0.3%
L 1
 
0.1%
Other values (2) 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 294
24.8%
H 291
24.6%
g 291
24.6%
h 291
24.6%
M 3
 
0.3%
e 3
 
0.3%
d 3
 
0.3%
u 3
 
0.3%
m 3
 
0.3%
L 1
 
0.1%
Other values (2) 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 294
24.8%
H 291
24.6%
g 291
24.6%
h 291
24.6%
M 3
 
0.3%
e 3
 
0.3%
d 3
 
0.3%
u 3
 
0.3%
m 3
 
0.3%
L 1
 
0.1%
Other values (2) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 294
24.8%
H 291
24.6%
g 291
24.6%
h 291
24.6%
M 3
 
0.3%
e 3
 
0.3%
d 3
 
0.3%
u 3
 
0.3%
m 3
 
0.3%
L 1
 
0.1%
Other values (2) 2
 
0.2%
Distinct275
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size43.8 KiB
2025-07-02T14:31:28.817615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length205
Median length131
Mean length91.91
Min length0

Characters and Unicode

Total characters27573
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique267 ?
Unique (%)89.0%

Sample

1st rowThe main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.
2nd rowThe main message is exceptionally clear, detailing who, what, and when without ambiguity.
3rd rowThe main message is very clear and direct, stating precisely what occurred and why.
4th rowThe main message is very clear and direct, stating the change, its source, and its impact.
5th rowThe message is clear and to the point, identifying the brand, action, and date.
ValueCountFrequency (%)
the 519
 
11.7%
and 354
 
8.0%
is 264
 
6.0%
clear 194
 
4.4%
message 190
 
4.3%
main 171
 
3.9%
to 156
 
3.5%
easy 124
 
2.8%
understand 120
 
2.7%
very 101
 
2.3%
Other values (776) 2231
50.4%
2025-07-02T14:31:29.326779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4129
15.0%
e 3094
11.2%
a 2300
 
8.3%
t 1850
 
6.7%
n 1803
 
6.5%
s 1776
 
6.4%
i 1752
 
6.4%
r 1227
 
4.5%
d 1103
 
4.0%
c 1003
 
3.6%
Other values (60) 7536
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4129
15.0%
e 3094
11.2%
a 2300
 
8.3%
t 1850
 
6.7%
n 1803
 
6.5%
s 1776
 
6.4%
i 1752
 
6.4%
r 1227
 
4.5%
d 1103
 
4.0%
c 1003
 
3.6%
Other values (60) 7536
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4129
15.0%
e 3094
11.2%
a 2300
 
8.3%
t 1850
 
6.7%
n 1803
 
6.5%
s 1776
 
6.4%
i 1752
 
6.4%
r 1227
 
4.5%
d 1103
 
4.0%
c 1003
 
3.6%
Other values (60) 7536
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4129
15.0%
e 3094
11.2%
a 2300
 
8.3%
t 1850
 
6.7%
n 1803
 
6.5%
s 1776
 
6.4%
i 1752
 
6.4%
r 1227
 
4.5%
d 1103
 
4.0%
c 1003
 
3.6%
Other values (60) 7536
27.3%

Relevance_and_Timeliness_Value
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
High
286 
Medium
 
9
 
5

Length

Max length6
Median length4
Mean length3.9933333
Min length0

Characters and Unicode

Total characters1198
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 286
95.3%
Medium 9
 
3.0%
5
 
1.7%

Length

2025-07-02T14:31:29.460250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:29.540201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 286
96.9%
medium 9
 
3.1%

Most occurring characters

ValueCountFrequency (%)
i 295
24.6%
H 286
23.9%
g 286
23.9%
h 286
23.9%
M 9
 
0.8%
e 9
 
0.8%
d 9
 
0.8%
u 9
 
0.8%
m 9
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 295
24.6%
H 286
23.9%
g 286
23.9%
h 286
23.9%
M 9
 
0.8%
e 9
 
0.8%
d 9
 
0.8%
u 9
 
0.8%
m 9
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 295
24.6%
H 286
23.9%
g 286
23.9%
h 286
23.9%
M 9
 
0.8%
e 9
 
0.8%
d 9
 
0.8%
u 9
 
0.8%
m 9
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 295
24.6%
H 286
23.9%
g 286
23.9%
h 286
23.9%
M 9
 
0.8%
e 9
 
0.8%
d 9
 
0.8%
u 9
 
0.8%
m 9
 
0.8%
Distinct296
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
2025-07-02T14:31:29.820747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length233
Median length150.5
Mean length115.74667
Min length0

Characters and Unicode

Total characters34724
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)98.3%

Sample

1st rowThe headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.
2nd rowHighly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.
3rd rowThe topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.
4th rowHighly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.
5th rowHighly relevant for McDonald's customers and tied to a specific future date, July 2.
ValueCountFrequency (%)
and 360
 
7.0%
a 206
 
4.0%
relevant 183
 
3.6%
to 148
 
2.9%
highly 140
 
2.7%
the 128
 
2.5%
of 121
 
2.4%
interest 118
 
2.3%
are 118
 
2.3%
is 104
 
2.0%
Other values (1033) 3486
68.2%
2025-07-02T14:31:30.451627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4817
13.9%
e 3779
 
10.9%
n 2552
 
7.3%
i 2523
 
7.3%
a 2429
 
7.0%
t 2231
 
6.4%
r 1915
 
5.5%
s 1785
 
5.1%
o 1633
 
4.7%
l 1505
 
4.3%
Other values (62) 9555
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4817
13.9%
e 3779
 
10.9%
n 2552
 
7.3%
i 2523
 
7.3%
a 2429
 
7.0%
t 2231
 
6.4%
r 1915
 
5.5%
s 1785
 
5.1%
o 1633
 
4.7%
l 1505
 
4.3%
Other values (62) 9555
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4817
13.9%
e 3779
 
10.9%
n 2552
 
7.3%
i 2523
 
7.3%
a 2429
 
7.0%
t 2231
 
6.4%
r 1915
 
5.5%
s 1785
 
5.1%
o 1633
 
4.7%
l 1505
 
4.3%
Other values (62) 9555
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4817
13.9%
e 3779
 
10.9%
n 2552
 
7.3%
i 2523
 
7.3%
a 2429
 
7.0%
t 2231
 
6.4%
r 1915
 
5.5%
s 1785
 
5.1%
o 1633
 
4.7%
l 1505
 
4.3%
Other values (62) 9555
27.5%

Strategic_Keyword_Usage_Value
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
High
289 
Medium
 
6
 
5

Length

Max length6
Median length4
Mean length3.9733333
Min length0

Characters and Unicode

Total characters1192
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 289
96.3%
Medium 6
 
2.0%
5
 
1.7%

Length

2025-07-02T14:31:30.634469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:30.734273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 289
98.0%
medium 6
 
2.0%

Most occurring characters

ValueCountFrequency (%)
i 295
24.7%
H 289
24.2%
g 289
24.2%
h 289
24.2%
M 6
 
0.5%
e 6
 
0.5%
d 6
 
0.5%
u 6
 
0.5%
m 6
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 295
24.7%
H 289
24.2%
g 289
24.2%
h 289
24.2%
M 6
 
0.5%
e 6
 
0.5%
d 6
 
0.5%
u 6
 
0.5%
m 6
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 295
24.7%
H 289
24.2%
g 289
24.2%
h 289
24.2%
M 6
 
0.5%
e 6
 
0.5%
d 6
 
0.5%
u 6
 
0.5%
m 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 295
24.7%
H 289
24.2%
g 289
24.2%
h 289
24.2%
M 6
 
0.5%
e 6
 
0.5%
d 6
 
0.5%
u 6
 
0.5%
m 6
 
0.5%
Distinct296
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size57.0 KiB
2025-07-02T14:31:31.084589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length229
Median length160
Mean length129.62667
Min length0

Characters and Unicode

Total characters38888
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)98.3%

Sample

1st rowKey terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.
2nd rowContains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.
3rd rowUses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.
4th rowUses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.
5th rowUses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.
ValueCountFrequency (%)
and 462
 
8.4%
keywords 252
 
4.6%
like 237
 
4.3%
are 233
 
4.2%
relevant 212
 
3.9%
highly 188
 
3.4%
to 155
 
2.8%
uses 143
 
2.6%
the 122
 
2.2%
which 106
 
1.9%
Other values (1353) 3376
61.5%
2025-07-02T14:31:31.711685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5191
 
13.3%
e 4088
 
10.5%
a 2689
 
6.9%
r 2239
 
5.8%
t 2049
 
5.3%
n 2044
 
5.3%
i 1999
 
5.1%
s 1930
 
5.0%
l 1640
 
4.2%
o 1548
 
4.0%
Other values (70) 13471
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5191
 
13.3%
e 4088
 
10.5%
a 2689
 
6.9%
r 2239
 
5.8%
t 2049
 
5.3%
n 2044
 
5.3%
i 1999
 
5.1%
s 1930
 
5.0%
l 1640
 
4.2%
o 1548
 
4.0%
Other values (70) 13471
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5191
 
13.3%
e 4088
 
10.5%
a 2689
 
6.9%
r 2239
 
5.8%
t 2049
 
5.3%
n 2044
 
5.3%
i 1999
 
5.1%
s 1930
 
5.0%
l 1640
 
4.2%
o 1548
 
4.0%
Other values (70) 13471
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5191
 
13.3%
e 4088
 
10.5%
a 2689
 
6.9%
r 2239
 
5.8%
t 2049
 
5.3%
n 2044
 
5.3%
i 1999
 
5.1%
s 1930
 
5.0%
l 1640
 
4.2%
o 1548
 
4.0%
Other values (70) 13471
34.6%

Originality_and_Differentiation_Value
Categorical

High correlation 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
Medium
210 
High
69 
Low
 
16
 
5

Length

Max length6
Median length6
Mean length5.28
Min length0

Characters and Unicode

Total characters1584
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 210
70.0%
High 69
 
23.0%
Low 16
 
5.3%
5
 
1.7%

Length

2025-07-02T14:31:31.885871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:31.991238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 210
71.2%
high 69
 
23.4%
low 16
 
5.4%

Most occurring characters

ValueCountFrequency (%)
i 279
17.6%
M 210
13.3%
e 210
13.3%
d 210
13.3%
u 210
13.3%
m 210
13.3%
H 69
 
4.4%
g 69
 
4.4%
h 69
 
4.4%
L 16
 
1.0%
Other values (2) 32
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 279
17.6%
M 210
13.3%
e 210
13.3%
d 210
13.3%
u 210
13.3%
m 210
13.3%
H 69
 
4.4%
g 69
 
4.4%
h 69
 
4.4%
L 16
 
1.0%
Other values (2) 32
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 279
17.6%
M 210
13.3%
e 210
13.3%
d 210
13.3%
u 210
13.3%
m 210
13.3%
H 69
 
4.4%
g 69
 
4.4%
h 69
 
4.4%
L 16
 
1.0%
Other values (2) 32
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 279
17.6%
M 210
13.3%
e 210
13.3%
d 210
13.3%
u 210
13.3%
m 210
13.3%
H 69
 
4.4%
g 69
 
4.4%
h 69
 
4.4%
L 16
 
1.0%
Other values (2) 32
 
2.0%
Distinct296
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size57.0 KiB
2025-07-02T14:31:32.322480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length255
Median length165
Mean length131.35667
Min length0

Characters and Unicode

Total characters39407
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)98.3%

Sample

1st rowWhile the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.
2nd rowWhile a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.
3rd rowThe specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.
4th rowWhile the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.
5th rowWhile common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.
ValueCountFrequency (%)
the 487
 
8.0%
a 334
 
5.5%
is 221
 
3.6%
and 210
 
3.4%
unique 161
 
2.6%
specific 158
 
2.6%
of 148
 
2.4%
common 145
 
2.4%
while 143
 
2.3%
it 116
 
1.9%
Other values (1215) 3968
65.1%
2025-07-02T14:31:32.909627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5796
14.7%
e 3845
 
9.8%
i 3148
 
8.0%
n 2637
 
6.7%
t 2582
 
6.6%
a 2529
 
6.4%
o 1977
 
5.0%
s 1901
 
4.8%
r 1697
 
4.3%
c 1378
 
3.5%
Other values (73) 11917
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5796
14.7%
e 3845
 
9.8%
i 3148
 
8.0%
n 2637
 
6.7%
t 2582
 
6.6%
a 2529
 
6.4%
o 1977
 
5.0%
s 1901
 
4.8%
r 1697
 
4.3%
c 1378
 
3.5%
Other values (73) 11917
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5796
14.7%
e 3845
 
9.8%
i 3148
 
8.0%
n 2637
 
6.7%
t 2582
 
6.6%
a 2529
 
6.4%
o 1977
 
5.0%
s 1901
 
4.8%
r 1697
 
4.3%
c 1378
 
3.5%
Other values (73) 11917
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5796
14.7%
e 3845
 
9.8%
i 3148
 
8.0%
n 2637
 
6.7%
t 2582
 
6.6%
a 2529
 
6.4%
o 1977
 
5.0%
s 1901
 
4.8%
r 1697
 
4.3%
c 1378
 
3.5%
Other values (73) 11917
30.2%

Contains_Numbers
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
151 
Yes
144 
 
5

Length

Max length3
Median length2
Mean length2.4466667
Min length0

Characters and Unicode

Total characters734
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 151
50.3%
Yes 144
48.0%
5
 
1.7%

Length

2025-07-02T14:31:33.099216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:33.246446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 151
51.2%
yes 144
48.8%

Most occurring characters

ValueCountFrequency (%)
N 151
20.6%
o 151
20.6%
Y 144
19.6%
e 144
19.6%
s 144
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 151
20.6%
o 151
20.6%
Y 144
19.6%
e 144
19.6%
s 144
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 151
20.6%
o 151
20.6%
Y 144
19.6%
e 144
19.6%
s 144
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 151
20.6%
o 151
20.6%
Y 144
19.6%
e 144
19.6%
s 144
19.6%

Contains_Quotes
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
255 
Yes
40 
 
5

Length

Max length3
Median length2
Mean length2.1
Min length0

Characters and Unicode

Total characters630
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 255
85.0%
Yes 40
 
13.3%
5
 
1.7%

Length

2025-07-02T14:31:33.380142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:33.829921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 255
86.4%
yes 40
 
13.6%

Most occurring characters

ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Contains_Question_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
283 
Yes
 
12
 
5

Length

Max length3
Median length2
Mean length2.0066667
Min length0

Characters and Unicode

Total characters602
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 283
94.3%
Yes 12
 
4.0%
5
 
1.7%

Length

2025-07-02T14:31:33.955343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.025069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 283
95.9%
yes 12
 
4.1%

Most occurring characters

ValueCountFrequency (%)
N 283
47.0%
o 283
47.0%
Y 12
 
2.0%
e 12
 
2.0%
s 12
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 283
47.0%
o 283
47.0%
Y 12
 
2.0%
e 12
 
2.0%
s 12
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 283
47.0%
o 283
47.0%
Y 12
 
2.0%
e 12
 
2.0%
s 12
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 283
47.0%
o 283
47.0%
Y 12
 
2.0%
e 12
 
2.0%
s 12
 
2.0%

Contains_Colon
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
255 
Yes
40 
 
5

Length

Max length3
Median length2
Mean length2.1
Min length0

Characters and Unicode

Total characters630
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 255
85.0%
Yes 40
 
13.3%
5
 
1.7%

Length

2025-07-02T14:31:34.113523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.182357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 255
86.4%
yes 40
 
13.6%

Most occurring characters

ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 255
40.5%
o 255
40.5%
Y 40
 
6.3%
e 40
 
6.3%
s 40
 
6.3%

Contains_Hyphen
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
228 
Yes
67 
 
5

Length

Max length3
Median length2
Mean length2.19
Min length0

Characters and Unicode

Total characters657
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 228
76.0%
Yes 67
 
22.3%
5
 
1.7%

Length

2025-07-02T14:31:34.289501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.361515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 228
77.3%
yes 67
 
22.7%

Most occurring characters

ValueCountFrequency (%)
N 228
34.7%
o 228
34.7%
Y 67
 
10.2%
e 67
 
10.2%
s 67
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 228
34.7%
o 228
34.7%
Y 67
 
10.2%
e 67
 
10.2%
s 67
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 228
34.7%
o 228
34.7%
Y 67
 
10.2%
e 67
 
10.2%
s 67
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 228
34.7%
o 228
34.7%
Y 67
 
10.2%
e 67
 
10.2%
s 67
 
10.2%

Contains_Exclamation_Mark
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
295 
 
5

Length

Max length2
Median length2
Mean length1.9666667
Min length0

Characters and Unicode

Total characters590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 295
98.3%
5
 
1.7%

Length

2025-07-02T14:31:34.454396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.523441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 295
100.0%

Most occurring characters

ValueCountFrequency (%)
N 295
50.0%
o 295
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 295
50.0%
o 295
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 295
50.0%
o 295
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 295
50.0%
o 295
50.0%

Starts_With_Number
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
274 
Yes
 
21
 
5

Length

Max length3
Median length2
Mean length2.0366667
Min length0

Characters and Unicode

Total characters611
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 274
91.3%
Yes 21
 
7.0%
5
 
1.7%

Length

2025-07-02T14:31:34.603485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.672110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 274
92.9%
yes 21
 
7.1%

Most occurring characters

ValueCountFrequency (%)
N 274
44.8%
o 274
44.8%
Y 21
 
3.4%
e 21
 
3.4%
s 21
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 274
44.8%
o 274
44.8%
Y 21
 
3.4%
e 21
 
3.4%
s 21
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 274
44.8%
o 274
44.8%
Y 21
 
3.4%
e 21
 
3.4%
s 21
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 274
44.8%
o 274
44.8%
Y 21
 
3.4%
e 21
 
3.4%
s 21
 
3.4%

Ends_With_Question_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
285 
Yes
 
10
 
5

Length

Max length3
Median length2
Mean length2
Min length0

Characters and Unicode

Total characters600
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 285
95.0%
Yes 10
 
3.3%
5
 
1.7%

Length

2025-07-02T14:31:34.762712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.834966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 285
96.6%
yes 10
 
3.4%

Most occurring characters

ValueCountFrequency (%)
N 285
47.5%
o 285
47.5%
Y 10
 
1.7%
e 10
 
1.7%
s 10
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 285
47.5%
o 285
47.5%
Y 10
 
1.7%
e 10
 
1.7%
s 10
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 285
47.5%
o 285
47.5%
Y 10
 
1.7%
e 10
 
1.7%
s 10
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 285
47.5%
o 285
47.5%
Y 10
 
1.7%
e 10
 
1.7%
s 10
 
1.7%

Length_General_Assessment
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
Adequate
277 
Too long, risk of truncation
 
18
 
5

Length

Max length28
Median length8
Mean length9.0666667
Min length0

Characters and Unicode

Total characters2720
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdequate
2nd rowAdequate
3rd rowAdequate
4th rowAdequate
5th rowAdequate

Common Values

ValueCountFrequency (%)
Adequate 277
92.3%
Too long, risk of truncation 18
 
6.0%
5
 
1.7%

Length

2025-07-02T14:31:34.912891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:34.990804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
adequate 277
75.5%
too 18
 
4.9%
long 18
 
4.9%
risk 18
 
4.9%
of 18
 
4.9%
truncation 18
 
4.9%

Most occurring characters

ValueCountFrequency (%)
e 554
20.4%
t 313
11.5%
u 295
10.8%
a 295
10.8%
q 277
10.2%
d 277
10.2%
A 277
10.2%
o 90
 
3.3%
72
 
2.6%
n 54
 
2.0%
Other values (10) 216
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 554
20.4%
t 313
11.5%
u 295
10.8%
a 295
10.8%
q 277
10.2%
d 277
10.2%
A 277
10.2%
o 90
 
3.3%
72
 
2.6%
n 54
 
2.0%
Other values (10) 216
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 554
20.4%
t 313
11.5%
u 295
10.8%
a 295
10.8%
q 277
10.2%
d 277
10.2%
A 277
10.2%
o 90
 
3.3%
72
 
2.6%
n 54
 
2.0%
Other values (10) 216
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 554
20.4%
t 313
11.5%
u 295
10.8%
a 295
10.8%
q 277
10.2%
d 277
10.2%
A 277
10.2%
o 90
 
3.3%
72
 
2.6%
n 54
 
2.0%
Other values (10) 216
 
7.9%
Distinct90
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
2025-07-02T14:31:35.237773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1433333
Min length0

Characters and Unicode

Total characters643
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)11.0%

Sample

1st row105
2nd row101
3rd row106
4th row173
5th row57
ValueCountFrequency (%)
50 14
 
4.7%
59 13
 
4.4%
90 11
 
3.7%
60 11
 
3.7%
69 10
 
3.4%
70 9
 
3.1%
73 8
 
2.7%
64 8
 
2.7%
62 8
 
2.7%
67 7
 
2.4%
Other values (79) 196
66.4%
2025-07-02T14:31:35.628649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 97
15.1%
1 84
13.1%
9 79
12.3%
6 78
12.1%
0 75
11.7%
7 67
10.4%
4 55
8.6%
8 43
6.7%
3 36
 
5.6%
2 29
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 97
15.1%
1 84
13.1%
9 79
12.3%
6 78
12.1%
0 75
11.7%
7 67
10.4%
4 55
8.6%
8 43
6.7%
3 36
 
5.6%
2 29
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 97
15.1%
1 84
13.1%
9 79
12.3%
6 78
12.1%
0 75
11.7%
7 67
10.4%
4 55
8.6%
8 43
6.7%
3 36
 
5.6%
2 29
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 97
15.1%
1 84
13.1%
9 79
12.3%
6 78
12.1%
0 75
11.7%
7 67
10.4%
4 55
8.6%
8 43
6.7%
3 36
 
5.6%
2 29
 
4.5%

Emphatic_Capitalization_Usage
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
No
280 
 
5
Yes, "The Show Must Go On", justified use as it is a direct quote.
 
1
Yes, 'DWTS', 'SYTYCD', justified use
 
1
Yes, 'Already Made A Payment', justified use within a quote to emphasize the person's statement.
 
1
Other values (12)
 
12

Length

Max length112
Median length2
Mean length4.6766667
Min length0

Characters and Unicode

Total characters1403
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)5.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 280
93.3%
5
 
1.7%
Yes, "The Show Must Go On", justified use as it is a direct quote. 1
 
0.3%
Yes, 'DWTS', 'SYTYCD', justified use 1
 
0.3%
Yes, 'Already Made A Payment', justified use within a quote to emphasize the person's statement. 1
 
0.3%
Yes, '11 Phrases Deeply Unhappy People Use On A Regular Basis', seeks impact 1
 
0.3%
Yes, 'No Touch', seeks impact 1
 
0.3%
Yes, 'EARLIER', seeks impact 1
 
0.3%
Yes, 'NY', justified use 1
 
0.3%
Yes, 'Best', 'All Time', seeks impact 1
 
0.3%
Other values (7) 7
 
2.3%

Length

2025-07-02T14:31:35.759700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 281
69.6%
yes 15
 
3.7%
impact 9
 
2.2%
seeks 9
 
2.2%
use 7
 
1.7%
justified 6
 
1.5%
a 4
 
1.0%
the 4
 
1.0%
and 3
 
0.7%
quote 2
 
0.5%
Other values (62) 64
 
15.8%

Most occurring characters

ValueCountFrequency (%)
o 302
21.5%
N 283
20.2%
109
 
7.8%
e 97
 
6.9%
s 69
 
4.9%
' 55
 
3.9%
, 46
 
3.3%
i 45
 
3.2%
t 39
 
2.8%
a 37
 
2.6%
Other values (43) 321
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 302
21.5%
N 283
20.2%
109
 
7.8%
e 97
 
6.9%
s 69
 
4.9%
' 55
 
3.9%
, 46
 
3.3%
i 45
 
3.2%
t 39
 
2.8%
a 37
 
2.6%
Other values (43) 321
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 302
21.5%
N 283
20.2%
109
 
7.8%
e 97
 
6.9%
s 69
 
4.9%
' 55
 
3.9%
, 46
 
3.3%
i 45
 
3.2%
t 39
 
2.8%
a 37
 
2.6%
Other values (43) 321
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 302
21.5%
N 283
20.2%
109
 
7.8%
e 97
 
6.9%
s 69
 
4.9%
' 55
 
3.9%
, 46
 
3.3%
i 45
 
3.2%
t 39
 
2.8%
a 37
 
2.6%
Other values (43) 321
22.9%

Main_Classification
Categorical

High correlation 

Distinct21
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size22.3 KiB
Declarative Simple
186 
Mystery/Revelation
24 
Attribution ('according to', 'reveals')
 
18
Direct Question
 
12
List/Numbered
 
10
Other values (16)
50 

Length

Max length52
Median length18
Mean length18.833333
Min length0

Characters and Unicode

Total characters5650
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.7%

Sample

1st rowDeclarative Simple
2nd rowDeclarative Simple
3rd rowDeclarative Simple
4th rowUrgency
5th rowDeclarative Simple

Common Values

ValueCountFrequency (%)
Declarative Simple 186
62.0%
Mystery/Revelation 24
 
8.0%
Attribution ('according to', 'reveals') 18
 
6.0%
Direct Question 12
 
4.0%
List/Numbered 10
 
3.3%
List/Numbered ('5 ways') 9
 
3.0%
Direct Quote 8
 
2.7%
5
 
1.7%
Comparative 5
 
1.7%
Urgency 4
 
1.3%
Other values (11) 19
 
6.3%

Length

2025-07-02T14:31:35.872814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
declarative 187
31.3%
simple 186
31.2%
mystery/revelation 28
 
4.7%
direct 22
 
3.7%
attribution 19
 
3.2%
to 19
 
3.2%
according 19
 
3.2%
reveals 19
 
3.2%
list/numbered 19
 
3.2%
question 14
 
2.3%
Other values (17) 65
 
10.9%

Most occurring characters

ValueCountFrequency (%)
e 807
14.3%
i 536
 
9.5%
a 469
 
8.3%
l 429
 
7.6%
t 426
 
7.5%
r 347
 
6.1%
302
 
5.3%
c 258
 
4.6%
v 248
 
4.4%
m 212
 
3.8%
Other values (29) 1616
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 807
14.3%
i 536
 
9.5%
a 469
 
8.3%
l 429
 
7.6%
t 426
 
7.5%
r 347
 
6.1%
302
 
5.3%
c 258
 
4.6%
v 248
 
4.4%
m 212
 
3.8%
Other values (29) 1616
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 807
14.3%
i 536
 
9.5%
a 469
 
8.3%
l 429
 
7.6%
t 426
 
7.5%
r 347
 
6.1%
302
 
5.3%
c 258
 
4.6%
v 248
 
4.4%
m 212
 
3.8%
Other values (29) 1616
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 807
14.3%
i 536
 
9.5%
a 469
 
8.3%
l 429
 
7.6%
t 426
 
7.5%
r 347
 
6.1%
302
 
5.3%
c 258
 
4.6%
v 248
 
4.4%
m 212
 
3.8%
Other values (29) 1616
28.6%
Distinct296
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size55.0 KiB
2025-07-02T14:31:36.208611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length305
Median length161.5
Mean length128.79
Min length0

Characters and Unicode

Total characters38637
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)98.3%

Sample

1st rowThe headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.
2nd rowThe headline directly states a new rule and its implications, serving as a straightforward announcement.
3rd rowThe headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.
4th rowThe headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.
5th rowThe headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.
ValueCountFrequency (%)
a 550
 
9.5%
the 467
 
8.0%
headline 272
 
4.7%
or 209
 
3.6%
without 153
 
2.6%
statement 118
 
2.0%
question 117
 
2.0%
and 110
 
1.9%
an 110
 
1.9%
about 108
 
1.9%
Other values (847) 3598
61.9%
2025-07-02T14:31:36.734293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5517
14.3%
e 3899
 
10.1%
t 3322
 
8.6%
a 2838
 
7.3%
i 2813
 
7.3%
n 2523
 
6.5%
s 1952
 
5.1%
o 1939
 
5.0%
r 1805
 
4.7%
h 1274
 
3.3%
Other values (64) 10755
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5517
14.3%
e 3899
 
10.1%
t 3322
 
8.6%
a 2838
 
7.3%
i 2813
 
7.3%
n 2523
 
6.5%
s 1952
 
5.1%
o 1939
 
5.0%
r 1805
 
4.7%
h 1274
 
3.3%
Other values (64) 10755
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5517
14.3%
e 3899
 
10.1%
t 3322
 
8.6%
a 2838
 
7.3%
i 2813
 
7.3%
n 2523
 
6.5%
s 1952
 
5.1%
o 1939
 
5.0%
r 1805
 
4.7%
h 1274
 
3.3%
Other values (64) 10755
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5517
14.3%
e 3899
 
10.1%
t 3322
 
8.6%
a 2838
 
7.3%
i 2813
 
7.3%
n 2523
 
6.5%
s 1952
 
5.1%
o 1939
 
5.0%
r 1805
 
4.7%
h 1274
 
3.3%
Other values (64) 10755
27.8%

Temporal_Urgency_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
250 
Yes
45 
 
5

Length

Max length3
Median length2
Mean length2.1166667
Min length0

Characters and Unicode

Total characters635
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 250
83.3%
Yes 45
 
15.0%
5
 
1.7%

Length

2025-07-02T14:31:36.854497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:36.923695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 250
84.7%
yes 45
 
15.3%

Most occurring characters

ValueCountFrequency (%)
N 250
39.4%
o 250
39.4%
Y 45
 
7.1%
e 45
 
7.1%
s 45
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 250
39.4%
o 250
39.4%
Y 45
 
7.1%
e 45
 
7.1%
s 45
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 250
39.4%
o 250
39.4%
Y 45
 
7.1%
e 45
 
7.1%
s 45
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 250
39.4%
o 250
39.4%
Y 45
 
7.1%
e 45
 
7.1%
s 45
 
7.1%

Temporal_Urgency_Words
Categorical

High correlation  Imbalance 

Distinct36
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size17.9 KiB
[]
250 
 
5
["Just"]
 
4
["Now"]
 
3
["abruptly"]
 
3
Other values (31)
35 

Length

Max length27
Median length2
Mean length3.7466667
Min length0

Characters and Unicode

Total characters1124
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)9.0%

Sample

1st row["immediately"]
2nd row["Begins July 2025"]
3rd row[]
4th row["immediate","urgently"]
5th row[]

Common Values

ValueCountFrequency (%)
[] 250
83.3%
5
 
1.7%
["Just"] 4
 
1.3%
["Now"] 3
 
1.0%
["abruptly"] 3
 
1.0%
["Already"] 2
 
0.7%
["immediately"] 2
 
0.7%
["suddenly"] 2
 
0.7%
["This Month"] 2
 
0.7%
["Begins July 2025"] 1
 
0.3%
Other values (26) 26
 
8.7%

Length

2025-07-02T14:31:37.020638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
250
78.6%
this 8
 
2.5%
just 4
 
1.3%
now 3
 
0.9%
abruptly 3
 
0.9%
month 3
 
0.9%
years 3
 
0.9%
immediately 2
 
0.6%
suddenly 2
 
0.6%
july 2
 
0.6%
Other values (34) 38
 
11.9%

Most occurring characters

ValueCountFrequency (%)
[ 295
26.2%
] 295
26.2%
" 106
 
9.4%
e 42
 
3.7%
t 30
 
2.7%
a 26
 
2.3%
i 26
 
2.3%
s 23
 
2.0%
23
 
2.0%
l 22
 
2.0%
Other values (40) 236
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 295
26.2%
] 295
26.2%
" 106
 
9.4%
e 42
 
3.7%
t 30
 
2.7%
a 26
 
2.3%
i 26
 
2.3%
s 23
 
2.0%
23
 
2.0%
l 22
 
2.0%
Other values (40) 236
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 295
26.2%
] 295
26.2%
" 106
 
9.4%
e 42
 
3.7%
t 30
 
2.7%
a 26
 
2.3%
i 26
 
2.3%
s 23
 
2.0%
23
 
2.0%
l 22
 
2.0%
Other values (40) 236
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 295
26.2%
] 295
26.2%
" 106
 
9.4%
e 42
 
3.7%
t 30
 
2.7%
a 26
 
2.3%
i 26
 
2.3%
s 23
 
2.0%
23
 
2.0%
l 22
 
2.0%
Other values (40) 236
21.0%

Exclusivity_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
288 
Yes
 
7
 
5

Length

Max length3
Median length2
Mean length1.99
Min length0

Characters and Unicode

Total characters597
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 288
96.0%
Yes 7
 
2.3%
5
 
1.7%

Length

2025-07-02T14:31:37.141203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:37.218818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 288
97.6%
yes 7
 
2.4%

Most occurring characters

ValueCountFrequency (%)
N 288
48.2%
o 288
48.2%
Y 7
 
1.2%
e 7
 
1.2%
s 7
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 288
48.2%
o 288
48.2%
Y 7
 
1.2%
e 7
 
1.2%
s 7
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 288
48.2%
o 288
48.2%
Y 7
 
1.2%
e 7
 
1.2%
s 7
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 288
48.2%
o 288
48.2%
Y 7
 
1.2%
e 7
 
1.2%
s 7
 
1.2%

Exclusivity_Words
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
[]
288 
 
5
["Limited-Edition"]
 
2
["first"]
 
1
["first","never before seen"]
 
1
Other values (3)
 
3

Length

Max length29
Median length2
Mean length2.29
Min length0

Characters and Unicode

Total characters687
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.7%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 288
96.0%
5
 
1.7%
["Limited-Edition"] 2
 
0.7%
["first"] 1
 
0.3%
["first","never before seen"] 1
 
0.3%
["only"] 1
 
0.3%
["unique"] 1
 
0.3%
["this US state"] 1
 
0.3%

Length

2025-07-02T14:31:37.305621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:37.414336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
288
96.3%
limited-edition 2
 
0.7%
first 1
 
0.3%
first","never 1
 
0.3%
before 1
 
0.3%
seen 1
 
0.3%
only 1
 
0.3%
unique 1
 
0.3%
this 1
 
0.3%
us 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
[ 295
42.9%
] 295
42.9%
" 16
 
2.3%
i 12
 
1.7%
e 10
 
1.5%
t 9
 
1.3%
n 6
 
0.9%
s 5
 
0.7%
4
 
0.6%
r 4
 
0.6%
Other values (18) 31
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 295
42.9%
] 295
42.9%
" 16
 
2.3%
i 12
 
1.7%
e 10
 
1.5%
t 9
 
1.3%
n 6
 
0.9%
s 5
 
0.7%
4
 
0.6%
r 4
 
0.6%
Other values (18) 31
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 295
42.9%
] 295
42.9%
" 16
 
2.3%
i 12
 
1.7%
e 10
 
1.5%
t 9
 
1.3%
n 6
 
0.9%
s 5
 
0.7%
4
 
0.6%
r 4
 
0.6%
Other values (18) 31
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 295
42.9%
] 295
42.9%
" 16
 
2.3%
i 12
 
1.7%
e 10
 
1.5%
t 9
 
1.3%
n 6
 
0.9%
s 5
 
0.7%
4
 
0.6%
r 4
 
0.6%
Other values (18) 31
 
4.5%

Authority_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
211 
Yes
84 
 
5

Length

Max length3
Median length2
Mean length2.2466667
Min length0

Characters and Unicode

Total characters674
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 211
70.3%
Yes 84
 
28.0%
5
 
1.7%

Length

2025-07-02T14:31:37.543459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:37.619384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 211
71.5%
yes 84
 
28.5%

Most occurring characters

ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%
Distinct76
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
2025-07-02T14:31:37.797772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length55
Median length2
Mean length6.3733333
Min length0

Characters and Unicode

Total characters1912
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)23.3%

Sample

1st row["CEO admits"]
2nd row[]
3rd row[]
4th row["DMV","confirms"]
5th row[]
ValueCountFrequency (%)
211
63.4%
experts 6
 
1.8%
tsa 5
 
1.5%
law 3
 
0.9%
social 3
 
0.9%
confirms 2
 
0.6%
ceo 2
 
0.6%
to 2
 
0.6%
scientists 2
 
0.6%
out 2
 
0.6%
Other values (90) 95
28.5%
2025-07-02T14:31:38.144315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 295
15.4%
] 295
15.4%
" 244
12.8%
e 96
 
5.0%
i 73
 
3.8%
s 68
 
3.6%
o 68
 
3.6%
r 64
 
3.3%
n 63
 
3.3%
t 62
 
3.2%
Other values (46) 584
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 295
15.4%
] 295
15.4%
" 244
12.8%
e 96
 
5.0%
i 73
 
3.8%
s 68
 
3.6%
o 68
 
3.6%
r 64
 
3.3%
n 63
 
3.3%
t 62
 
3.2%
Other values (46) 584
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 295
15.4%
] 295
15.4%
" 244
12.8%
e 96
 
5.0%
i 73
 
3.8%
s 68
 
3.6%
o 68
 
3.6%
r 64
 
3.3%
n 63
 
3.3%
t 62
 
3.2%
Other values (46) 584
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 295
15.4%
] 295
15.4%
" 244
12.8%
e 96
 
5.0%
i 73
 
3.8%
s 68
 
3.6%
o 68
 
3.6%
r 64
 
3.3%
n 63
 
3.3%
t 62
 
3.2%
Other values (46) 584
30.5%

Solution_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
244 
Yes
51 
 
5

Length

Max length3
Median length2
Mean length2.1366667
Min length0

Characters and Unicode

Total characters641
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 244
81.3%
Yes 51
 
17.0%
5
 
1.7%

Length

2025-07-02T14:31:38.266066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:38.342519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 244
82.7%
yes 51
 
17.3%

Most occurring characters

ValueCountFrequency (%)
N 244
38.1%
o 244
38.1%
Y 51
 
8.0%
e 51
 
8.0%
s 51
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 244
38.1%
o 244
38.1%
Y 51
 
8.0%
e 51
 
8.0%
s 51
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 244
38.1%
o 244
38.1%
Y 51
 
8.0%
e 51
 
8.0%
s 51
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 244
38.1%
o 244
38.1%
Y 51
 
8.0%
e 51
 
8.0%
s 51
 
8.0%

Solution_Words
Categorical

High correlation  Imbalance 

Distinct50
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
[]
244 
 
5
["How to"]
 
2
["How To Clean"]
 
2
["Restored"]
 
2
Other values (45)
45 

Length

Max length44
Median length2
Mean length4.5966667
Min length0

Characters and Unicode

Total characters1379
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)15.0%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 244
81.3%
5
 
1.7%
["How to"] 2
 
0.7%
["How To Clean"] 2
 
0.7%
["Restored"] 2
 
0.7%
["method","without effort"] 1
 
0.3%
["build","indestructible"] 1
 
0.3%
["Turn It Around","Real Estate Investments"] 1
 
0.3%
["enable new security features"] 1
 
0.3%
["Way"] 1
 
0.3%
Other values (40) 40
 
13.3%

Length

2025-07-02T14:31:38.461796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
244
69.7%
how 10
 
2.9%
to 10
 
2.9%
it 3
 
0.9%
new 3
 
0.9%
way 3
 
0.9%
best 3
 
0.9%
restored 2
 
0.6%
use 2
 
0.6%
them 2
 
0.6%
Other values (67) 68
 
19.4%

Most occurring characters

ValueCountFrequency (%)
[ 295
21.4%
] 295
21.4%
" 134
9.7%
e 82
 
5.9%
55
 
4.0%
t 54
 
3.9%
o 47
 
3.4%
s 35
 
2.5%
a 31
 
2.2%
r 30
 
2.2%
Other values (43) 321
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 295
21.4%
] 295
21.4%
" 134
9.7%
e 82
 
5.9%
55
 
4.0%
t 54
 
3.9%
o 47
 
3.4%
s 35
 
2.5%
a 31
 
2.2%
r 30
 
2.2%
Other values (43) 321
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 295
21.4%
] 295
21.4%
" 134
9.7%
e 82
 
5.9%
55
 
4.0%
t 54
 
3.9%
o 47
 
3.4%
s 35
 
2.5%
a 31
 
2.2%
r 30
 
2.2%
Other values (43) 321
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 295
21.4%
] 295
21.4%
" 134
9.7%
e 82
 
5.9%
55
 
4.0%
t 54
 
3.9%
o 47
 
3.4%
s 35
 
2.5%
a 31
 
2.2%
r 30
 
2.2%
Other values (43) 321
23.3%

Economic_Benefit_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
253 
Yes
42 
 
5

Length

Max length3
Median length2
Mean length2.1066667
Min length0

Characters and Unicode

Total characters632
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 253
84.3%
Yes 42
 
14.0%
5
 
1.7%

Length

2025-07-02T14:31:38.574655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:38.650631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 253
85.8%
yes 42
 
14.2%

Most occurring characters

ValueCountFrequency (%)
N 253
40.0%
o 253
40.0%
Y 42
 
6.6%
e 42
 
6.6%
s 42
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 253
40.0%
o 253
40.0%
Y 42
 
6.6%
e 42
 
6.6%
s 42
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 253
40.0%
o 253
40.0%
Y 42
 
6.6%
e 42
 
6.6%
s 42
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 253
40.0%
o 253
40.0%
Y 42
 
6.6%
e 42
 
6.6%
s 42
 
6.6%

Economic_Benefit_Words
Categorical

High correlation  Imbalance 

Distinct42
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
[]
253 
 
5
["cheaper"]
 
2
["refunds"]
 
2
["$300","settlement"]
 
1
Other values (37)
37 

Length

Max length37
Median length2
Mean length4.1
Min length0

Characters and Unicode

Total characters1230
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)12.7%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 253
84.3%
5
 
1.7%
["cheaper"] 2
 
0.7%
["refunds"] 2
 
0.7%
["$300","settlement"] 1
 
0.3%
["buy","Dollar Tree"] 1
 
0.3%
["$1.8 Million"] 1
 
0.3%
["$5 Million"] 1
 
0.3%
["Full Benefits"] 1
 
0.3%
["$5 billion"] 1
 
0.3%
Other values (32) 32
 
10.7%

Length

2025-07-02T14:31:38.762956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
253
78.6%
million 5
 
1.6%
refunds 3
 
0.9%
billion 3
 
0.9%
generous 2
 
0.6%
benefits 2
 
0.6%
5 2
 
0.6%
cheaper 2
 
0.6%
fines 2
 
0.6%
1 2
 
0.6%
Other values (46) 46
 
14.3%

Most occurring characters

ValueCountFrequency (%)
[ 295
24.0%
] 295
24.0%
" 116
 
9.4%
e 45
 
3.7%
i 43
 
3.5%
l 36
 
2.9%
n 32
 
2.6%
o 29
 
2.4%
s 28
 
2.3%
27
 
2.2%
Other values (43) 284
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 295
24.0%
] 295
24.0%
" 116
 
9.4%
e 45
 
3.7%
i 43
 
3.5%
l 36
 
2.9%
n 32
 
2.6%
o 29
 
2.4%
s 28
 
2.3%
27
 
2.2%
Other values (43) 284
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 295
24.0%
] 295
24.0%
" 116
 
9.4%
e 45
 
3.7%
i 43
 
3.5%
l 36
 
2.9%
n 32
 
2.6%
o 29
 
2.4%
s 28
 
2.3%
27
 
2.2%
Other values (43) 284
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 295
24.0%
] 295
24.0%
" 116
 
9.4%
e 45
 
3.7%
i 43
 
3.5%
l 36
 
2.9%
n 32
 
2.6%
o 29
 
2.4%
s 28
 
2.3%
27
 
2.2%
Other values (43) 284
23.1%

Prohibition_Restriction_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
No
265 
Yes
30 
 
5

Length

Max length3
Median length2
Mean length2.0666667
Min length0

Characters and Unicode

Total characters620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 265
88.3%
Yes 30
 
10.0%
5
 
1.7%

Length

2025-07-02T14:31:38.887208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:38.963886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 265
89.8%
yes 30
 
10.2%

Most occurring characters

ValueCountFrequency (%)
N 265
42.7%
o 265
42.7%
Y 30
 
4.8%
e 30
 
4.8%
s 30
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 265
42.7%
o 265
42.7%
Y 30
 
4.8%
e 30
 
4.8%
s 30
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 265
42.7%
o 265
42.7%
Y 30
 
4.8%
e 30
 
4.8%
s 30
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 265
42.7%
o 265
42.7%
Y 30
 
4.8%
e 30
 
4.8%
s 30
 
4.8%

Prohibition_Restriction_Words
Categorical

High correlation  Imbalance 

Distinct30
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
[]
265 
 
5
["Banned"]
 
2
["bans"]
 
2
["ending the benefit","without immediate renewal"]
 
1
Other values (25)
 
25

Length

Max length68
Median length2
Mean length3.8733333
Min length0

Characters and Unicode

Total characters1162
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)8.7%

Sample

1st row[]
2nd row[]
3rd row["denied"]
4th row["ending the benefit","without immediate renewal"]
5th row[]

Common Values

ValueCountFrequency (%)
[] 265
88.3%
5
 
1.7%
["Banned"] 2
 
0.7%
["bans"] 2
 
0.7%
["ending the benefit","without immediate renewal"] 1
 
0.3%
["will have to meet new requirements","keep their driver's license"] 1
 
0.3%
["Too Risqué","for Radio Play"] 1
 
0.3%
["Goodbye","illegal","no longer be used"] 1
 
0.3%
["Dispose of Items"] 1
 
0.3%
["Neither"] 1
 
0.3%
Other values (20) 20
 
6.7%

Length

2025-07-02T14:31:39.073465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
265
78.9%
to 3
 
0.9%
banned 2
 
0.6%
the 2
 
0.6%
bans 2
 
0.6%
neither 2
 
0.6%
away 2
 
0.6%
renewal 1
 
0.3%
benefit","without 1
 
0.3%
will 1
 
0.3%
Other values (55) 55
 
16.4%

Most occurring characters

ValueCountFrequency (%)
[ 295
25.4%
] 295
25.4%
" 82
 
7.1%
e 66
 
5.7%
n 41
 
3.5%
41
 
3.5%
i 37
 
3.2%
o 34
 
2.9%
t 29
 
2.5%
r 27
 
2.3%
Other values (31) 215
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 295
25.4%
] 295
25.4%
" 82
 
7.1%
e 66
 
5.7%
n 41
 
3.5%
41
 
3.5%
i 37
 
3.2%
o 34
 
2.9%
t 29
 
2.5%
r 27
 
2.3%
Other values (31) 215
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 295
25.4%
] 295
25.4%
" 82
 
7.1%
e 66
 
5.7%
n 41
 
3.5%
41
 
3.5%
i 37
 
3.2%
o 34
 
2.9%
t 29
 
2.5%
r 27
 
2.3%
Other values (31) 215
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 295
25.4%
] 295
25.4%
" 82
 
7.1%
e 66
 
5.7%
n 41
 
3.5%
41
 
3.5%
i 37
 
3.2%
o 34
 
2.9%
t 29
 
2.5%
r 27
 
2.3%
Other values (31) 215
18.5%

National_Relevance_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
189 
Yes
106 
 
5

Length

Max length3
Median length2
Mean length2.32
Min length0

Characters and Unicode

Total characters696
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 189
63.0%
Yes 106
35.3%
5
 
1.7%

Length

2025-07-02T14:31:39.515776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:39.587523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 189
64.1%
yes 106
35.9%

Most occurring characters

ValueCountFrequency (%)
N 189
27.2%
o 189
27.2%
Y 106
15.2%
e 106
15.2%
s 106
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 189
27.2%
o 189
27.2%
Y 106
15.2%
e 106
15.2%
s 106
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 189
27.2%
o 189
27.2%
Y 106
15.2%
e 106
15.2%
s 106
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 189
27.2%
o 189
27.2%
Y 106
15.2%
e 106
15.2%
s 106
15.2%
Distinct74
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2025-07-02T14:31:39.697097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length113
Median length2
Mean length7.7533333
Min length0

Characters and Unicode

Total characters2326
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)20.3%

Sample

1st row[]
2nd row["U.S."]
3rd row[]
4th row["United States"]
5th row[]
ValueCountFrequency (%)
189
54.8%
states 11
 
3.2%
new 11
 
3.2%
york 10
 
2.9%
state 10
 
2.9%
ny 6
 
1.7%
us 6
 
1.7%
california 5
 
1.4%
u.s 5
 
1.4%
united 4
 
1.2%
Other values (77) 88
25.5%
2025-07-02T14:31:39.972214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 346
14.9%
[ 295
12.7%
] 295
12.7%
a 141
 
6.1%
i 105
 
4.5%
e 103
 
4.4%
t 103
 
4.4%
n 80
 
3.4%
r 78
 
3.4%
, 67
 
2.9%
Other values (46) 713
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 346
14.9%
[ 295
12.7%
] 295
12.7%
a 141
 
6.1%
i 105
 
4.5%
e 103
 
4.4%
t 103
 
4.4%
n 80
 
3.4%
r 78
 
3.4%
, 67
 
2.9%
Other values (46) 713
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 346
14.9%
[ 295
12.7%
] 295
12.7%
a 141
 
6.1%
i 105
 
4.5%
e 103
 
4.4%
t 103
 
4.4%
n 80
 
3.4%
r 78
 
3.4%
, 67
 
2.9%
Other values (46) 713
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 346
14.9%
[ 295
12.7%
] 295
12.7%
a 141
 
6.1%
i 105
 
4.5%
e 103
 
4.4%
t 103
 
4.4%
n 80
 
3.4%
r 78
 
3.4%
, 67
 
2.9%
Other values (46) 713
30.7%

Recognized_Brand_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
177 
Yes
118 
 
5

Length

Max length3
Median length2
Mean length2.36
Min length0

Characters and Unicode

Total characters708
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 177
59.0%
Yes 118
39.3%
5
 
1.7%

Length

2025-07-02T14:31:40.106251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:40.183292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 177
60.0%
yes 118
40.0%

Most occurring characters

ValueCountFrequency (%)
N 177
25.0%
o 177
25.0%
Y 118
16.7%
e 118
16.7%
s 118
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 177
25.0%
o 177
25.0%
Y 118
16.7%
e 118
16.7%
s 118
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 177
25.0%
o 177
25.0%
Y 118
16.7%
e 118
16.7%
s 118
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 177
25.0%
o 177
25.0%
Y 118
16.7%
e 118
16.7%
s 118
16.7%
Distinct95
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
2025-07-02T14:31:40.344978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length118
Median length2
Mean length9.1533333
Min length0

Characters and Unicode

Total characters2746
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)27.7%

Sample

1st row["Ford"]
2nd row[]
3rd row[]
4th row["DMV"]
5th row["McDonald's"]
ValueCountFrequency (%)
177
44.8%
security 8
 
2.0%
social 7
 
1.8%
walmart 7
 
1.8%
tsa 5
 
1.3%
mcdonald's 3
 
0.8%
costco 3
 
0.8%
air 3
 
0.8%
tree 2
 
0.5%
walgreens 2
 
0.5%
Other values (162) 178
45.1%
2025-07-02T14:31:40.733490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 364
 
13.3%
[ 295
 
10.7%
] 295
 
10.7%
e 146
 
5.3%
a 141
 
5.1%
r 114
 
4.2%
i 102
 
3.7%
100
 
3.6%
o 98
 
3.6%
l 89
 
3.2%
Other values (59) 1002
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 364
 
13.3%
[ 295
 
10.7%
] 295
 
10.7%
e 146
 
5.3%
a 141
 
5.1%
r 114
 
4.2%
i 102
 
3.7%
100
 
3.6%
o 98
 
3.6%
l 89
 
3.2%
Other values (59) 1002
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 364
 
13.3%
[ 295
 
10.7%
] 295
 
10.7%
e 146
 
5.3%
a 141
 
5.1%
r 114
 
4.2%
i 102
 
3.7%
100
 
3.6%
o 98
 
3.6%
l 89
 
3.2%
Other values (59) 1002
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 364
 
13.3%
[ 295
 
10.7%
] 295
 
10.7%
e 146
 
5.3%
a 141
 
5.1%
r 114
 
4.2%
i 102
 
3.7%
100
 
3.6%
o 98
 
3.6%
l 89
 
3.2%
Other values (59) 1002
36.5%

Curiosity_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Yes
222 
No
73 
 
5

Length

Max length3
Median length3
Mean length2.7066667
Min length0

Characters and Unicode

Total characters812
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 222
74.0%
No 73
 
24.3%
5
 
1.7%

Length

2025-07-02T14:31:40.865819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:40.940728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 222
75.3%
no 73
 
24.7%

Most occurring characters

ValueCountFrequency (%)
Y 222
27.3%
e 222
27.3%
s 222
27.3%
N 73
 
9.0%
o 73
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 222
27.3%
e 222
27.3%
s 222
27.3%
N 73
 
9.0%
o 73
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 222
27.3%
e 222
27.3%
s 222
27.3%
N 73
 
9.0%
o 73
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 222
27.3%
e 222
27.3%
s 222
27.3%
N 73
 
9.0%
o 73
 
9.0%
Distinct223
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size51.1 KiB
2025-07-02T14:31:41.223636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length273
Median length190
Mean length104.4
Min length0

Characters and Unicode

Total characters31320
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)74.0%

Sample

1st row
2nd rowThe phrase 'Essential Changes' prompts readers to seek information on what those changes entail.
3rd row
4th row
5th rowThe headline creates an information gap by not revealing which specific breakfast item is being removed.
ValueCountFrequency (%)
the 495
 
9.9%
and 167
 
3.3%
to 165
 
3.3%
an 154
 
3.1%
gap 153
 
3.1%
information 151
 
3.0%
creates 143
 
2.9%
reader 111
 
2.2%
about 109
 
2.2%
a 84
 
1.7%
Other values (1078) 3272
65.4%
2025-07-02T14:31:41.733657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4782
15.3%
e 3100
 
9.9%
a 2342
 
7.5%
t 2149
 
6.9%
i 1858
 
5.9%
n 1855
 
5.9%
r 1781
 
5.7%
o 1770
 
5.7%
s 1498
 
4.8%
h 1266
 
4.0%
Other values (72) 8919
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4782
15.3%
e 3100
 
9.9%
a 2342
 
7.5%
t 2149
 
6.9%
i 1858
 
5.9%
n 1855
 
5.9%
r 1781
 
5.7%
o 1770
 
5.7%
s 1498
 
4.8%
h 1266
 
4.0%
Other values (72) 8919
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4782
15.3%
e 3100
 
9.9%
a 2342
 
7.5%
t 2149
 
6.9%
i 1858
 
5.9%
n 1855
 
5.9%
r 1781
 
5.7%
o 1770
 
5.7%
s 1498
 
4.8%
h 1266
 
4.0%
Other values (72) 8919
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4782
15.3%
e 3100
 
9.9%
a 2342
 
7.5%
t 2149
 
6.9%
i 1858
 
5.9%
n 1855
 
5.9%
r 1781
 
5.7%
o 1770
 
5.7%
s 1498
 
4.8%
h 1266
 
4.0%
Other values (72) 8919
28.5%

Fear_Concern_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
164 
Yes
131 
 
5

Length

Max length3
Median length2
Mean length2.4033333
Min length0

Characters and Unicode

Total characters721
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 164
54.7%
Yes 131
43.7%
5
 
1.7%

Length

2025-07-02T14:31:41.866854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:41.936163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 164
55.6%
yes 131
44.4%

Most occurring characters

ValueCountFrequency (%)
N 164
22.7%
o 164
22.7%
Y 131
18.2%
e 131
18.2%
s 131
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 164
22.7%
o 164
22.7%
Y 131
18.2%
e 131
18.2%
s 131
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 164
22.7%
o 164
22.7%
Y 131
18.2%
e 131
18.2%
s 131
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 164
22.7%
o 164
22.7%
Y 131
18.2%
e 131
18.2%
s 131
18.2%
Distinct133
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
2025-07-02T14:31:42.259417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length240
Median length0
Mean length57.16
Min length0

Characters and Unicode

Total characters17148
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)44.0%

Sample

1st rowThe phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.
2nd row
3rd row
4th rowThe phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.
5th rowThe phrase 'Removing... for Good' could evoke concern among customers about a favorite item.
ValueCountFrequency (%)
the 172
 
6.7%
and 116
 
4.5%
concern 116
 
4.5%
of 81
 
3.2%
a 74
 
2.9%
or 62
 
2.4%
for 60
 
2.3%
about 52
 
2.0%
evoke 50
 
2.0%
to 47
 
1.8%
Other values (771) 1730
67.6%
2025-07-02T14:31:42.809793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2428
14.2%
e 1746
 
10.2%
n 1232
 
7.2%
o 1125
 
6.6%
a 1087
 
6.3%
t 998
 
5.8%
i 987
 
5.8%
r 965
 
5.6%
s 858
 
5.0%
c 742
 
4.3%
Other values (59) 4980
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2428
14.2%
e 1746
 
10.2%
n 1232
 
7.2%
o 1125
 
6.6%
a 1087
 
6.3%
t 998
 
5.8%
i 987
 
5.8%
r 965
 
5.6%
s 858
 
5.0%
c 742
 
4.3%
Other values (59) 4980
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2428
14.2%
e 1746
 
10.2%
n 1232
 
7.2%
o 1125
 
6.6%
a 1087
 
6.3%
t 998
 
5.8%
i 987
 
5.8%
r 965
 
5.6%
s 858
 
5.0%
c 742
 
4.3%
Other values (59) 4980
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2428
14.2%
e 1746
 
10.2%
n 1232
 
7.2%
o 1125
 
6.6%
a 1087
 
6.3%
t 998
 
5.8%
i 987
 
5.8%
r 965
 
5.6%
s 858
 
5.0%
c 742
 
4.3%
Other values (59) 4980
29.0%

Surprise_Awe_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
197 
Yes
98 
 
5

Length

Max length3
Median length2
Mean length2.2933333
Min length0

Characters and Unicode

Total characters688
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 197
65.7%
Yes 98
32.7%
5
 
1.7%

Length

2025-07-02T14:31:42.930838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:43.006460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 197
66.8%
yes 98
33.2%

Most occurring characters

ValueCountFrequency (%)
N 197
28.6%
o 197
28.6%
Y 98
14.2%
e 98
14.2%
s 98
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 197
28.6%
o 197
28.6%
Y 98
14.2%
e 98
14.2%
s 98
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 197
28.6%
o 197
28.6%
Y 98
14.2%
e 98
14.2%
s 98
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 197
28.6%
o 197
28.6%
Y 98
14.2%
e 98
14.2%
s 98
14.2%
Distinct99
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
2025-07-02T14:31:43.324821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length206
Median length0
Mean length38.413333
Min length0

Characters and Unicode

Total characters11524
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)32.7%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
the 165
 
9.0%
of 99
 
5.4%
and 84
 
4.6%
a 79
 
4.3%
surprise 50
 
2.7%
is 32
 
1.7%
evoke 32
 
1.7%
can 32
 
1.7%
unexpected 31
 
1.7%
surprising 30
 
1.6%
Other values (660) 1197
65.4%
2025-07-02T14:31:43.832335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1733
15.0%
e 1211
 
10.5%
n 752
 
6.5%
a 728
 
6.3%
i 710
 
6.2%
s 683
 
5.9%
t 645
 
5.6%
o 602
 
5.2%
r 588
 
5.1%
l 347
 
3.0%
Other values (66) 3525
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1733
15.0%
e 1211
 
10.5%
n 752
 
6.5%
a 728
 
6.3%
i 710
 
6.2%
s 683
 
5.9%
t 645
 
5.6%
o 602
 
5.2%
r 588
 
5.1%
l 347
 
3.0%
Other values (66) 3525
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1733
15.0%
e 1211
 
10.5%
n 752
 
6.5%
a 728
 
6.3%
i 710
 
6.2%
s 683
 
5.9%
t 645
 
5.6%
o 602
 
5.2%
r 588
 
5.1%
l 347
 
3.0%
Other values (66) 3525
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1733
15.0%
e 1211
 
10.5%
n 752
 
6.5%
a 728
 
6.3%
i 710
 
6.2%
s 683
 
5.9%
t 645
 
5.6%
o 602
 
5.2%
r 588
 
5.1%
l 347
 
3.0%
Other values (66) 3525
30.6%

Indignation_Controversy_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
242 
Yes
53 
 
5

Length

Max length3
Median length2
Mean length2.1433333
Min length0

Characters and Unicode

Total characters643
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 242
80.7%
Yes 53
 
17.7%
5
 
1.7%

Length

2025-07-02T14:31:43.991938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:44.112037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 242
82.0%
yes 53
 
18.0%

Most occurring characters

ValueCountFrequency (%)
N 242
37.6%
o 242
37.6%
Y 53
 
8.2%
e 53
 
8.2%
s 53
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 242
37.6%
o 242
37.6%
Y 53
 
8.2%
e 53
 
8.2%
s 53
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 242
37.6%
o 242
37.6%
Y 53
 
8.2%
e 53
 
8.2%
s 53
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 242
37.6%
o 242
37.6%
Y 53
 
8.2%
e 53
 
8.2%
s 53
 
8.2%
Distinct54
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size25.7 KiB
2025-07-02T14:31:44.501157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length253
Median length0
Mean length23.96
Min length0

Characters and Unicode

Total characters7188
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)17.7%

Sample

1st row
2nd row
3rd rowThe phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.
4th row
5th row
ValueCountFrequency (%)
the 65
 
6.1%
and 53
 
5.0%
of 36
 
3.4%
a 36
 
3.4%
indignation 35
 
3.3%
to 29
 
2.7%
debate 28
 
2.6%
provoke 27
 
2.5%
or 26
 
2.4%
can 20
 
1.9%
Other values (425) 713
66.8%
2025-07-02T14:31:45.169802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1015
14.1%
e 646
 
9.0%
n 567
 
7.9%
i 524
 
7.3%
a 515
 
7.2%
o 507
 
7.1%
t 455
 
6.3%
r 364
 
5.1%
s 329
 
4.6%
d 257
 
3.6%
Other values (58) 2009
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1015
14.1%
e 646
 
9.0%
n 567
 
7.9%
i 524
 
7.3%
a 515
 
7.2%
o 507
 
7.1%
t 455
 
6.3%
r 364
 
5.1%
s 329
 
4.6%
d 257
 
3.6%
Other values (58) 2009
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1015
14.1%
e 646
 
9.0%
n 567
 
7.9%
i 524
 
7.3%
a 515
 
7.2%
o 507
 
7.1%
t 455
 
6.3%
r 364
 
5.1%
s 329
 
4.6%
d 257
 
3.6%
Other values (58) 2009
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1015
14.1%
e 646
 
9.0%
n 567
 
7.9%
i 524
 
7.3%
a 515
 
7.2%
o 507
 
7.1%
t 455
 
6.3%
r 364
 
5.1%
s 329
 
4.6%
d 257
 
3.6%
Other values (58) 2009
27.9%

Hope_Optimism_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.5 KiB
No
211 
Yes
84 
 
5

Length

Max length3
Median length2
Mean length2.2466667
Min length0

Characters and Unicode

Total characters674
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 211
70.3%
Yes 84
 
28.0%
5
 
1.7%

Length

2025-07-02T14:31:45.361164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:45.471608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 211
71.5%
yes 84
 
28.5%

Most occurring characters

ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 211
31.3%
o 211
31.3%
Y 84
 
12.5%
e 84
 
12.5%
s 84
 
12.5%
Distinct85
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2025-07-02T14:31:45.913437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length186
Median length0
Mean length31.47
Min length0

Characters and Unicode

Total characters9441
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)28.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
a 117
 
8.1%
the 86
 
5.9%
and 62
 
4.3%
for 61
 
4.2%
of 54
 
3.7%
positive 44
 
3.0%
hope 42
 
2.9%
to 30
 
2.1%
offers 23
 
1.6%
sense 18
 
1.2%
Other values (502) 912
62.9%
2025-07-02T14:31:46.617009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1365
14.5%
e 1034
 
11.0%
o 714
 
7.6%
i 615
 
6.5%
a 538
 
5.7%
s 535
 
5.7%
t 514
 
5.4%
n 501
 
5.3%
r 487
 
5.2%
l 286
 
3.0%
Other values (52) 2852
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1365
14.5%
e 1034
 
11.0%
o 714
 
7.6%
i 615
 
6.5%
a 538
 
5.7%
s 535
 
5.7%
t 514
 
5.4%
n 501
 
5.3%
r 487
 
5.2%
l 286
 
3.0%
Other values (52) 2852
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1365
14.5%
e 1034
 
11.0%
o 714
 
7.6%
i 615
 
6.5%
a 538
 
5.7%
s 535
 
5.7%
t 514
 
5.4%
n 501
 
5.3%
r 487
 
5.2%
l 286
 
3.0%
Other values (52) 2852
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1365
14.5%
e 1034
 
11.0%
o 714
 
7.6%
i 615
 
6.5%
a 538
 
5.7%
s 535
 
5.7%
t 514
 
5.4%
n 501
 
5.3%
r 487
 
5.2%
l 286
 
3.0%
Other values (52) 2852
30.2%

Personal_Identification_Present
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Yes
231 
No
64 
 
5

Length

Max length3
Median length3
Mean length2.7366667
Min length0

Characters and Unicode

Total characters821
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 231
77.0%
No 64
 
21.3%
5
 
1.7%

Length

2025-07-02T14:31:46.795726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T14:31:46.907046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 231
78.3%
no 64
 
21.7%

Most occurring characters

ValueCountFrequency (%)
Y 231
28.1%
e 231
28.1%
s 231
28.1%
N 64
 
7.8%
o 64
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 821
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 231
28.1%
e 231
28.1%
s 231
28.1%
N 64
 
7.8%
o 64
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 821
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 231
28.1%
e 231
28.1%
s 231
28.1%
N 64
 
7.8%
o 64
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 821
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 231
28.1%
e 231
28.1%
s 231
28.1%
N 64
 
7.8%
o 64
 
7.8%
Distinct232
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Memory size47.3 KiB
2025-07-02T14:31:47.378058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length242
Median length172
Mean length101.12333
Min length0

Characters and Unicode

Total characters30337
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)77.0%

Sample

1st row
2nd rowDirectly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
3rd rowReaders who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
4th rowThe terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
5th rowAppeals directly to regular McDonald's breakfast consumers who might be affected by the change.
ValueCountFrequency (%)
the 328
 
7.1%
to 176
 
3.8%
of 169
 
3.7%
and 169
 
3.7%
a 116
 
2.5%
or 115
 
2.5%
directly 111
 
2.4%
with 105
 
2.3%
in 78
 
1.7%
personal 67
 
1.5%
Other values (991) 3185
69.0%
2025-07-02T14:31:48.072477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4388
14.5%
e 3293
 
10.9%
t 2062
 
6.8%
i 2035
 
6.7%
a 1954
 
6.4%
n 1889
 
6.2%
o 1799
 
5.9%
r 1718
 
5.7%
s 1590
 
5.2%
l 1197
 
3.9%
Other values (63) 8412
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4388
14.5%
e 3293
 
10.9%
t 2062
 
6.8%
i 2035
 
6.7%
a 1954
 
6.4%
n 1889
 
6.2%
o 1799
 
5.9%
r 1718
 
5.7%
s 1590
 
5.2%
l 1197
 
3.9%
Other values (63) 8412
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4388
14.5%
e 3293
 
10.9%
t 2062
 
6.8%
i 2035
 
6.7%
a 1954
 
6.4%
n 1889
 
6.2%
o 1799
 
5.9%
r 1718
 
5.7%
s 1590
 
5.2%
l 1197
 
3.9%
Other values (63) 8412
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4388
14.5%
e 3293
 
10.9%
t 2062
 
6.8%
i 2035
 
6.7%
a 1954
 
6.4%
n 1889
 
6.2%
o 1799
 
5.9%
r 1718
 
5.7%
s 1590
 
5.2%
l 1197
 
3.9%
Other values (63) 8412
27.7%

Interactions

2025-07-02T14:31:21.882465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-02T14:31:48.224693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Authority_PresentClarity_and_Conciseness_ValueContains_ColonContains_Exclamation_MarkContains_HyphenContains_NumbersContains_Question_MarkContains_QuotesCuriosity_PresentEconomic_Benefit_PresentEconomic_Benefit_WordsEmphatic_Capitalization_UsageEnds_With_Question_MarkExclusivity_PresentExclusivity_WordsFear_Concern_PresentHope_Optimism_PresentIndignation_Controversy_PresentLength_General_AssessmentMain_CategoryMain_ClassificationNational_Relevance_PresentOriginality_and_Differentiation_ValuePersonal_Identification_PresentProhibition_Restriction_PresentProhibition_Restriction_WordsRecognized_Brand_PresentRelevance_and_Timeliness_ValueSolution_PresentSolution_WordsStarts_With_NumberStrategic_Keyword_Usage_ValueSubcategory_2Surprise_Awe_PresentTemporal_Urgency_PresentTemporal_Urgency_WordsVisibility
Authority_Present1.0000.7050.7050.9980.7100.7050.7110.7050.7050.7090.6750.6860.7100.7050.7010.7110.7050.7080.7060.7110.7180.7070.7110.7070.7170.6970.7050.7060.7060.6590.7060.7080.6850.7060.7050.6741.000
Clarity_and_Conciseness_Value0.7051.0000.7040.9970.7030.7040.7030.7040.7040.7110.8040.5320.7030.7020.5600.7040.7030.7030.7740.5860.5240.7050.5890.7070.7030.4890.7040.7020.7030.4150.7030.7200.4930.7030.7100.7461.000
Contains_Colon0.7050.7041.0000.9980.7120.7060.7050.7090.7190.7080.6820.6970.7070.7110.7170.7070.7050.7050.7090.6980.7680.7050.7160.7110.7050.6750.7100.7060.7090.6800.7050.7050.7040.7190.7070.6961.000
Contains_Exclamation_Mark0.9980.9970.9981.0000.9980.9980.9980.9980.9980.9980.9300.9750.9980.9980.9900.9980.9980.9980.9980.9780.9680.9980.9970.9980.9980.9520.9980.9980.9980.9160.9980.9980.9450.9980.9980.9411.000
Contains_Hyphen0.7100.7030.7120.9981.0000.7060.7090.7100.7060.7070.6680.6920.7080.7070.7070.7050.7120.7120.7060.7110.7000.7050.7100.7060.7070.6890.7050.7050.7050.6460.7050.7050.6880.7050.7050.6771.000
Contains_Numbers0.7050.7040.7060.9980.7061.0000.7110.7050.7090.7170.6610.6890.7090.7050.7010.7050.7050.7050.7050.7020.7020.7050.7090.7060.7070.6730.7050.7050.7100.6520.7330.7050.6720.7170.7050.6731.000
Contains_Question_Mark0.7110.7030.7050.9980.7090.7111.0000.7070.7100.7050.6390.6710.9560.7050.6930.7050.7080.7080.7060.7030.9690.7060.7060.7090.7060.6390.7150.7050.7050.6800.7060.7050.6460.7070.7070.6241.000
Contains_Quotes0.7050.7040.7090.9980.7100.7050.7071.0000.7110.7050.6440.7200.7070.7050.7020.7110.7050.7060.7050.7080.7720.7090.7260.7060.7060.6910.7070.7080.7070.6280.7070.7070.6800.7140.7050.6611.000
Curiosity_Present0.7050.7040.7190.9980.7060.7090.7100.7111.0000.7050.6560.6870.7090.7050.6990.7190.7050.7060.7050.7320.7130.7250.7440.7060.7060.6680.7050.7050.7050.6500.7050.7050.6830.7300.7060.6691.000
Economic_Benefit_Present0.7090.7110.7080.9980.7070.7170.7050.7050.7051.0000.9320.6810.7050.7050.7010.7110.7250.7090.7120.7140.6820.7060.7080.7080.7070.6590.7060.7050.7050.6430.7070.7050.6780.7140.7050.6701.000
Economic_Benefit_Words0.6750.8040.6820.9300.6680.6610.6390.6440.6560.9321.0000.0000.6450.6610.3910.6550.6900.6820.7270.1480.1340.6630.5330.6740.6350.0000.6590.6900.6540.0000.6270.6700.1900.6720.6580.1691.000
Emphatic_Capitalization_Usage0.6860.5320.6970.9750.6920.6890.6710.7200.6870.6810.0001.0000.6710.6710.3030.6880.6910.6910.6720.2310.1950.6900.5470.6800.6940.3100.6870.6710.6970.3970.7040.6710.0750.6870.7010.2831.000
Ends_With_Question_Mark0.7100.7030.7070.9980.7080.7090.9560.7070.7090.7050.6450.6711.0000.7050.6930.7050.7070.7070.7060.7060.9230.7060.7080.7080.7060.6390.7130.7050.7050.6250.7060.7050.6320.7080.7070.6231.000
Exclusivity_Present0.7050.7020.7110.9980.7070.7050.7050.7050.7050.7050.6610.6710.7051.0000.9920.7060.7050.7050.7120.6950.6670.7050.7040.7050.7050.6910.7060.7050.7050.6410.7050.7050.6610.7070.7050.6761.000
Exclusivity_Words0.7010.5600.7170.9900.7070.7010.6930.7020.6990.7010.3910.3030.6930.9921.0000.7010.6980.7050.7300.3930.3180.7020.5680.7030.7040.4400.7020.6930.7000.3540.6930.6930.3690.7020.6960.3161.000
Fear_Concern_Present0.7110.7040.7070.9980.7050.7050.7050.7110.7190.7110.6550.6880.7050.7060.7011.0000.7320.7350.7090.7840.7010.7210.7040.7150.7250.6840.7050.7070.7080.6470.7060.7050.6890.7080.7060.6731.000
Hope_Optimism_Present0.7050.7030.7050.9980.7120.7050.7080.7050.7050.7250.6900.6910.7070.7050.6980.7321.0000.7160.7050.7520.6950.7090.7030.7090.7050.6720.7060.7090.7850.7310.7050.7080.6980.7050.7070.6661.000
Indignation_Controversy_Present0.7080.7030.7050.9980.7120.7050.7080.7060.7060.7090.6820.6910.7070.7050.7050.7350.7161.0000.7120.7270.6990.7120.7170.7050.7180.7040.7050.7050.7120.6180.7050.7060.6770.7340.7050.6561.000
Length_General_Assessment0.7060.7740.7090.9980.7060.7050.7060.7050.7050.7120.7270.6720.7060.7120.7300.7090.7050.7121.0000.6930.6930.7070.7030.7050.7050.6600.7070.7050.7050.6300.7060.7060.7050.7050.7050.6701.000
Main_Category0.7110.5860.6980.9780.7110.7020.7030.7080.7320.7140.1480.2310.7060.6950.3930.7840.7520.7270.6931.0000.3180.7760.5900.7360.7310.2120.7550.7000.7700.3380.7030.6900.4620.7090.6920.2101.000
Main_Classification0.7180.5240.7680.9680.7000.7020.9690.7720.7130.6820.1340.1950.9230.6670.3180.7010.6950.6990.6930.3181.0000.6900.5960.6940.6990.3160.7080.6650.7040.4450.8010.6690.2330.7230.7050.1971.000
National_Relevance_Present0.7070.7050.7050.9980.7050.7050.7060.7090.7250.7060.6630.6900.7060.7050.7020.7210.7090.7120.7070.7760.6901.0000.7060.7050.7100.6820.7080.7090.7100.6400.7050.7060.7120.7090.7080.6751.000
Originality_and_Differentiation_Value0.7110.5890.7160.9970.7100.7090.7060.7260.7440.7080.5330.5470.7080.7040.5680.7040.7030.7170.7030.5900.5960.7061.0000.7260.7060.5350.7090.7030.7030.5160.7040.7180.5480.8160.7020.5481.000
Personal_Identification_Present0.7070.7070.7110.9980.7060.7060.7090.7060.7060.7080.6740.6800.7080.7050.7030.7150.7090.7050.7050.7360.6940.7050.7261.0000.7130.6530.7050.7080.7080.6310.7060.7050.6920.7420.7050.6641.000
Prohibition_Restriction_Present0.7170.7030.7050.9980.7070.7070.7060.7060.7060.7070.6350.6940.7060.7050.7040.7250.7050.7180.7050.7310.6990.7100.7060.7131.0000.9530.7050.7060.7050.6500.7080.7060.6810.7070.7090.6861.000
Prohibition_Restriction_Words0.6970.4890.6750.9520.6890.6730.6390.6910.6680.6590.0000.3100.6390.6910.4400.6840.6720.7040.6600.2120.3160.6820.5350.6530.9531.0000.6740.6380.6750.2200.6400.6380.0000.6790.6940.2751.000
Recognized_Brand_Present0.7050.7040.7100.9980.7050.7050.7150.7070.7050.7060.6590.6870.7130.7060.7020.7050.7060.7050.7070.7550.7080.7080.7090.7050.7050.6741.0000.7050.7180.6430.7150.7050.6890.7050.7060.6681.000
Relevance_and_Timeliness_Value0.7060.7020.7060.9980.7050.7050.7050.7080.7050.7050.6900.6710.7050.7050.6930.7070.7090.7050.7050.7000.6650.7090.7030.7080.7060.6380.7051.0000.7070.5850.7060.7270.7110.7050.7070.6231.000
Solution_Present0.7060.7030.7090.9980.7050.7100.7050.7070.7050.7050.6540.6970.7050.7050.7000.7080.7850.7120.7050.7700.7040.7100.7030.7080.7050.6750.7180.7071.0000.9170.7050.7060.7170.7080.7050.6651.000
Solution_Words0.6590.4150.6800.9160.6460.6520.6800.6280.6500.6430.0000.3970.6250.6410.3540.6470.7310.6180.6300.3380.4450.6400.5160.6310.6500.2200.6430.5850.9171.0000.6580.5840.2910.6400.6350.0971.000
Starts_With_Number0.7060.7030.7050.9980.7050.7330.7060.7070.7050.7070.6270.7040.7060.7050.6930.7060.7050.7050.7060.7030.8010.7050.7040.7060.7080.6400.7150.7060.7050.6581.0000.7050.6930.7050.7100.6261.000
Strategic_Keyword_Usage_Value0.7080.7200.7050.9980.7050.7050.7050.7070.7050.7050.6700.6710.7050.7050.6930.7050.7080.7060.7060.6900.6690.7060.7180.7050.7060.6380.7050.7270.7060.5840.7051.0000.6300.7060.7060.6221.000
Subcategory_20.6850.4930.7040.9450.6880.6720.6460.6800.6830.6780.1900.0750.6320.6610.3690.6890.6980.6770.7050.4620.2330.7120.5480.6920.6810.0000.6890.7110.7170.2910.6930.6301.0000.6780.6520.0001.000
Surprise_Awe_Present0.7060.7030.7190.9980.7050.7170.7070.7140.7300.7140.6720.6870.7080.7070.7020.7080.7050.7340.7050.7090.7230.7090.8160.7420.7070.6790.7050.7050.7080.6400.7050.7060.6781.0000.7120.6861.000
Temporal_Urgency_Present0.7050.7100.7070.9980.7050.7050.7070.7050.7060.7050.6580.7010.7070.7050.6960.7060.7070.7050.7050.6920.7050.7080.7020.7050.7090.6940.7060.7070.7050.6350.7100.7060.6520.7121.0000.9431.000
Temporal_Urgency_Words0.6740.7460.6960.9410.6770.6730.6240.6610.6690.6700.1690.2830.6230.6760.3160.6730.6660.6560.6700.2100.1970.6750.5480.6640.6860.2750.6680.6230.6650.0970.6260.6220.0000.6860.9431.0001.000
Visibility1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2025-07-02T14:31:24.209581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-02T14:31:24.662927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TitleVisibilityCleaned_HeadlineMain_CategorySubcategory_1Subcategory_2Clarity_and_Conciseness_ValueClarity_and_Conciseness_CommentRelevance_and_Timeliness_ValueRelevance_and_Timeliness_CommentStrategic_Keyword_Usage_ValueStrategic_Keyword_Usage_CommentOriginality_and_Differentiation_ValueOriginality_and_Differentiation_CommentContains_NumbersContains_QuotesContains_Question_MarkContains_ColonContains_HyphenContains_Exclamation_MarkStarts_With_NumberEnds_With_Question_MarkLength_General_AssessmentLength_Number_of_CharactersEmphatic_Capitalization_UsageMain_ClassificationComment_Type_JustificationTemporal_Urgency_PresentTemporal_Urgency_WordsExclusivity_PresentExclusivity_WordsAuthority_PresentAuthority_WordsSolution_PresentSolution_WordsEconomic_Benefit_PresentEconomic_Benefit_WordsProhibition_Restriction_PresentProhibition_Restriction_WordsNational_Relevance_PresentNational_Relevance_WordsRecognized_Brand_PresentRecognized_Brand_WordsCuriosity_PresentCuriosity_EvidenceFear_Concern_PresentFear_Concern_EvidenceSurprise_Awe_PresentSurprise_Awe_EvidenceIndignation_Controversy_PresentIndignation_Controversy_EvidenceHope_Optimism_PresentHope_Optimism_EvidencePersonal_Identification_PresentPersonal_Identification_Evidence
0Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand22572066Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brandFinance_and_BusinessCompanies & EntrepreneurshipN/AHighThe main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.HighThe headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.HighKey terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.MediumWhile the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.NoYesNoNoNoNoNoNoAdequate105NoDeclarative SimpleThe headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.Yes["immediately"]No[]Yes["CEO admits"]No[]No[]No[]No[]Yes["Ford"]NoYesThe phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.NoNoNoNo
1New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above21331409New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and AboveNews_and_Current_EventsPoliticsGovernmentHighThe main message is exceptionally clear, detailing who, what, and when without ambiguity.HighHighly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.HighContains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.MediumWhile a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.YesNoNoNoYesNoNoNoAdequate101NoDeclarative SimpleThe headline directly states a new rule and its implications, serving as a straightforward announcement.Yes["Begins July 2025"]No[]No[]No[]No[]No[]Yes["U.S."]No[]YesThe phrase 'Essential Changes' prompts readers to seek information on what those changes entail.NoNoNoNoYesDirectly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
2Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender19344936Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgenderNews_and_Current_EventsPoliticsGovernmentHighThe main message is very clear and direct, stating precisely what occurred and why.HighThe topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.HighUses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.MediumThe specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.NoNoNoNoNoNoNoNoAdequate106NoDeclarative SimpleThe headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.No[]No[]No[]No[]No[]Yes["denied"]No[]No[]NoNoNoYesThe phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.NoYesReaders who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
3The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently18797641The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgentlyNews_and_Current_EventsPoliticsGovernmentHighThe main message is very clear and direct, stating the change, its source, and its impact.HighHighly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.HighUses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.MediumWhile the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.YesNoNoNoYesNoNoNoAdequate173NoUrgencyThe headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.Yes["immediate","urgently"]No[]Yes["DMV","confirms"]No[]No[]Yes["ending the benefit","without immediate renewal"]Yes["United States"]Yes["DMV"]NoYesThe phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.NoNoNoYesThe terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
4McDonald's Removing 1 Breakfast Menu Item for Good on July 216353543McDonald's Removing 1 Breakfast Menu Item for Good on July 2GastronomyRestaurants & ChefsN/AHighThe message is clear and to the point, identifying the brand, action, and date.HighHighly relevant for McDonald's customers and tied to a specific future date, July 2.HighUses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.MediumWhile common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.YesNoNoNoNoNoNoNoAdequate57NoDeclarative SimpleThe headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.No[]No[]No[]No[]No[]No[]No[]Yes["McDonald's"]YesThe headline creates an information gap by not revealing which specific breakfast item is being removed.YesThe phrase 'Removing... for Good' could evoke concern among customers about a favorite item.NoNoNoYesAppeals directly to regular McDonald's breakfast consumers who might be affected by the change.
5The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United States15318643The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United StatesNews_and_Current_EventsPoliticsGovernmentHighThe main message about new DMV requirements for older drivers is very clear and easy to understand.HighDriving regulations, especially those affecting a specific age group, are highly relevant to a large demographic and are a recurring topic of public interest.HighKeywords like 'DMV', '70', 'driver's license', and 'United States' are highly relevant and likely to be searched for or noticed by the target audience.MediumThe headline is specific about the authority ('DMV confirms it') and the demographic, which gives it some differentiation from generic news, but the structure is standard.YesNoNoNoYesNoNoNoAdequate106NoDeclarative SimpleThe headline directly states a fact confirmed by an authority, informing the reader of a new regulation without posing a question or implying a mystery.No[]No[]Yes["confirms","DMV"]No[]No[]Yes["will have to meet new requirements","keep their driver's license"]Yes["United States"]Yes["DMV"]YesThe mention of 'new requirements' without specifying them creates an information gap, prompting readers to click to learn what these changes entail.YesThe phrase 'will have to meet new requirements to keep their driver's license' can evoke concern or anxiety for older drivers about their ability to retain their driving privileges and independence.NoNoNoYesThe headline directly addresses and impacts 'people over 70', creating strong personal identification for individuals in that age group and their families.
6Goodbye to retirement at 65: Social Security sets a new retirement age from 202614627061Goodbye to retirement at 65: Social Security sets a new retirement age from 2026Finance_and_BusinessPersonal_FinanceRetirementHighThe main message is immediately clear: the retirement age is changing.HighThe topic of retirement age and Social Security is highly relevant to a broad audience and includes a specific future date ('from 2026') indicating timeliness.HighKey terms like 'retirement,' 'Social Security,' and 'retirement age' are highly strategic and likely to be searched by interested individuals.MediumWhile retirement changes are common news, the opening phrase 'Goodbye to retirement at 65' adds an impactful and somewhat unique framing.YesNoNoYesNoNoNoNoAdequate70NoDeclarative SimpleThe headline presents a direct statement of fact about a change in the retirement age.Yes["from 2026"]No[]Yes["Social Security"]No[]No[]No[]No[]Yes["Social Security"]YesThe phrase 'Goodbye to retirement at 65' immediately piques curiosity about what the new age will be and why the change is happening.YesThe headline could evoke concern among readers about their financial future and the implications of a later retirement.YesThe news of a fundamental shift in retirement age, a long-established concept, can be surprising.YesChanges to social programs like retirement age frequently generate public debate and potential indignation.NoYesThe topic of retirement and Social Security directly affects the financial planning and future of many individuals, leading to strong personal identification.
7Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.13507301Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.Finance_and_BusinessCompanies & EntrepreneurshipN/AHighThe main message is very clear, detailing the involved parties, the offer, the rejection, and the subsequent action, without ambiguity.HighFeatures highly relevant and frequently discussed figures (Elon Musk, Tim Cook) and companies (Apple), ensuring strong general interest.HighIncludes prominent keywords such as 'Elon Musk', 'Apple', 'Tim Cook', and '$5 billion', which are highly searchable and appealing to a broad audience.HighThe narrative of a public challenge, rejection, and explicit follow-through on a 'threat' provides a unique and dramatic angle that stands out.YesNoNoNoNoNoNoNoAdequate104NoDeclarative SimpleThe headline presents a straightforward statement of facts without posing a question, quoting directly, or explicitly indicating urgency.Yes["72 hours"]No[]No[]No[]Yes["$5 billion"]No[]No[]Yes["Elon Musk","Apple","Tim Cook"]YesThe phrase 'followed through with his threat' creates an information gap, compelling the reader to wonder what the threat was and its consequences.NoYesThe audacious $5 billion offer with a short ultimatum and the dramatic follow-through on a 'threat' can evoke surprise.YesThe nature of the 'threat' and its execution can spark debate or strong opinions about the involved parties' actions.NoNo
890s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”1333995290s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”Entertainment_and_CultureCelebrities_and_InfluencersN/AHighThe main message is straightforward and easy to understand, describing a highly unusual and specific event.HighThe headline describes an unusual and attention-grabbing incident involving a celebrity, which is highly relevant to current interests in entertainment news.HighKeywords like "90s Country Icon," "Teeth Fall Out," and "Mid-Performance" are highly specific and engaging, likely to capture audience attention.HighThe event described is extremely unusual and therefore the headline is highly original and stands out from typical news.YesYesNoYesNoNoYesNoAdequate101Yes, "The Show Must Go On", justified use as it is a direct quote.Direct QuoteThe headline prominently features a direct quote from the event, which is essential to its content and impact.No[]No[]No[]No[]No[]No[]Yes["Washington"]No[]YesThe phrase "Teeth Fall Out Mid-Performance" creates a strong information gap and immediately makes the reader curious about the circumstances.NoYesThe highly unexpected and bizarre nature of "Teeth Fall Out Mid-Performance" elicits surprise and possibly a sense of awe at the unusual incident.NoNoNo
9Americans who own refrigerators from 3 brands to get $300 from settlement13255807Americans who own refrigerators from 3 brands to get $300 from settlementFinance_and_BusinessPersonal_FinanceN/AHighThe main message is very clear: certain Americans will receive money due to a settlement.HighDirectly impacts a large segment of the population (Americans who own specific appliances) and offers a tangible benefit (money from a settlement).HighIncludes highly relevant keywords like 'Americans', 'refrigerators', 'brands', '$300', and 'settlement', which are likely search terms for interested individuals.MediumWhile the specific settlement details are unique, the headline structure of 'who gets money from X' is a common news format. The unnamed brands reduce its immediate differentiation.YesNoNoNoNoNoNoNoAdequate70NoDeclarative SimpleThe headline presents a direct and straightforward statement of fact without asking a question, making a comparison, or implying urgency.No[]No[]No[]No[]Yes["$300","settlement"]No[]Yes["Americans"]No[]YesThe mention of '3 brands' creates a curiosity gap, prompting readers to click to find out which brands are involved.NoNoNoYesThe prospect of receiving $300 from a settlement evokes a sense of hope and financial optimism for those who qualify.YesAmericans who own refrigerators' directly targets a broad demographic, encouraging self-identification and relevance.
TitleVisibilityCleaned_HeadlineMain_CategorySubcategory_1Subcategory_2Clarity_and_Conciseness_ValueClarity_and_Conciseness_CommentRelevance_and_Timeliness_ValueRelevance_and_Timeliness_CommentStrategic_Keyword_Usage_ValueStrategic_Keyword_Usage_CommentOriginality_and_Differentiation_ValueOriginality_and_Differentiation_CommentContains_NumbersContains_QuotesContains_Question_MarkContains_ColonContains_HyphenContains_Exclamation_MarkStarts_With_NumberEnds_With_Question_MarkLength_General_AssessmentLength_Number_of_CharactersEmphatic_Capitalization_UsageMain_ClassificationComment_Type_JustificationTemporal_Urgency_PresentTemporal_Urgency_WordsExclusivity_PresentExclusivity_WordsAuthority_PresentAuthority_WordsSolution_PresentSolution_WordsEconomic_Benefit_PresentEconomic_Benefit_WordsProhibition_Restriction_PresentProhibition_Restriction_WordsNational_Relevance_PresentNational_Relevance_WordsRecognized_Brand_PresentRecognized_Brand_WordsCuriosity_PresentCuriosity_EvidenceFear_Concern_PresentFear_Concern_EvidenceSurprise_Awe_PresentSurprise_Awe_EvidenceIndignation_Controversy_PresentIndignation_Controversy_EvidenceHope_Optimism_PresentHope_Optimism_EvidencePersonal_Identification_PresentPersonal_Identification_Evidence
290How To Clean A Glass Top Stove, According To An Expert2036769How To Clean A Glass Top Stove, According To An ExpertHome_and_LifestyleCleaning & OrganizationTipsHighThe main message is very clear and easy to understand.HighThis is an evergreen topic highly relevant to homeowners with glass top stoves.HighUses common search terms like "How To Clean" and "Glass Top Stove".MediumWhile "How To" is common, the "According To An Expert" adds a degree of differentiation and credibility.NoNoNoNoNoNoNoNoAdequate55NoAttribution ('according to', 'reveals')The headline specifies that the cleaning method is "According To An Expert," emphasizing credibility and derived knowledge.No[]No[]Yes["Expert"]Yes["How To Clean"]No[]No[]No[]No[]YesThe "How To" structure combined with the "According To An Expert" creates an information gap, making the reader curious about the expert's specific method.NoNoNoYesImplies a solution to a common household problem, offering hope for an effective cleaning method.YesAppeals directly to anyone who owns or uses a glass top stove and seeks a reliable cleaning solution.
291Shrimp, Corn, Potatoes, and Smoked Sausage Foil Pack2032696Shrimp, Corn, Potatoes, and Smoked Sausage Foil PackGastronomyRecipesMain CoursesHighClearly describes the components of the dish.HighEvergreen topic, appealing to anyone interested in cooking and meal preparation.HighContains specific ingredients and cooking method as keywords, highly searchable.MediumDescribes a common type of dish, but the combination is specific enough.NoNoNoNoNoNoNoNoAdequate50NoDeclarative SimpleThe headline simply states what the dish is, without asking a question, making a strong claim, or implying mystery.No[]No[]No[]No[]No[]No[]No[]No[]NoNoNoNoNoYesAppeals to those interested in home cooking and specific types of meals (foil packs, comfort food ingredients).
292Government Reverses Cuts for Thousands of Retirees - Social Security Benefits Restored Under New Rule2026378Government Reverses Cuts for Thousands of Retirees - Social Security Benefits Restored Under New RuleFinance_and_BusinessPersonal FinanceN/AHighThe headline clearly states the action (government reverses cuts) and the beneficiaries (retirees, social security benefits restored).HighSocial Security and retiree benefits are topics of broad and consistent interest, making it highly relevant.HighUses clear keywords like "Government", "Retirees", "Social Security Benefits", "Restored", which are highly searchable and relevant.MediumWhile clear and informative, the headline is fairly straightforward and doesn't employ a particularly unique or surprising angle. It's a standard news announcement.YesNoNoNoYesNoNoNoAdequate90NoDeclarative SimpleIt directly states a fact or event without posing a question, quoting someone, or indicating urgency in an emphatic way.No[]No[]Yes["Government","Rule"]Yes["Restored"]Yes["Cuts","Benefits"]No[]No[]Yes["Social Security"]NoYesThe word 'Cuts' implies a previous negative situation, which could evoke concern.NoNoYesWords like 'Reverses', 'Restored', and 'Benefits' evoke a sense of hope and relief for retirees.YesRefers to 'Thousands of Retirees' and 'Social Security Benefits', directly impacting a large demographic.
29310 phrases deeply unhappy people often use in daily conversation202244710 phrases deeply unhappy people often use in daily conversationHealth_and_WellnessMental HealthN/AHighThe message is clear and easy to understand, directly stating the content.HighDeals with an evergreen topic of human behavior and mental state, highly relevant to personal well-being.HighUses direct and relatable keywords like 'unhappy people' and 'daily conversation' that resonate with the target audience.MediumWhile listicles are common, focusing on specific 'phrases' offers a slightly distinct angle compared to general 'signs'.YesNoNoNoNoNoYesNoAdequate64NoList/Numbered ('5 ways')The headline begins with a number followed by the topic, indicating a listicle format.No[]No[]No[]No[]No[]No[]No[]No[]YesThe phrase '10 phrases deeply unhappy people often use' creates an information gap, making the reader curious about what those specific phrases are.YesThe mention of 'deeply unhappy people' can evoke concern or self-reflection, especially if the reader identifies with or knows someone described.NoNoNoYesThe topic of 'unhappy people' and phrases used in 'daily conversation' encourages personal identification or identification with someone the reader knows.
294Carnival Cruise Line suddenly cancels sailing2019272Carnival Cruise Line suddenly cancels sailingTravelPlanning & TipsN/AHighThe main message is immediately clear, stating the subject (Carnival Cruise Line) and the action (cancels sailing).HighHighly relevant to individuals interested in travel, especially those planning or affected by cruises. It conveys immediate, impactful news.HighUses highly relevant keywords like "Carnival Cruise Line" and "cancels sailing", which are directly searched by affected customers or interested parties.MediumThe headline is a straightforward factual report. While the specific event is unique, the phrasing is generic for a cancellation announcement.NoNoNoNoNoNoNoNoAdequate40NoDeclarative SimpleIt directly informs the reader about an event without posing a question, making a comparison, or building mystery.Yes["suddenly"]No[]No[]No[]No[]No[]No[]Yes["Carnival Cruise Line"]YesThe word 'suddenly' creates an information gap, making the reader wonder about the unexpected nature and reasons for the cancellation.YesThe cancellation of a sailing directly triggers fear and concern for passengers who may have booked or planned to travel with Carnival Cruise Line.YesThe term 'suddenly' implies an unexpected event, generating surprise regarding the abrupt nature of the cancellation.YesA sudden cancellation, especially of a large-scale event like a cruise sailing, can evoke indignation or frustration from affected customers.NoYesReaders who have booked or considered booking with Carnival Cruise Line, or who are generally interested in cruise travel, can personally identify with the impact of such news.
295Over 300 Flights Cancelled and Delayed as Air France, American, Oman Air, Singapore, Qatar, Emirates, United and More Face New Disruptions at Heathrow, Schiphol and Charles de Gaulle Due to Operational Challenges2018517Over 300 Flights Cancelled and Delayed as Air France, American, Oman Air, Singapore, Qatar, Emirates, United and More Face New Disruptions at Heathrow, Schiphol and Charles de Gaulle Due to Operational ChallengesNews_and_Current_EventsInternationalN/AHighThe main message regarding flight cancellations and delays is clear and direct, immediately conveying the core issue and identifying the affected entities.HighAir travel disruptions are a highly relevant and timely topic for a broad audience, especially those with travel plans or connections to the mentioned airlines and airports.HighThe headline effectively uses keywords such as "Flights Cancelled," "Delayed," specific airline names, and airport names, which are highly relevant and searchable.MediumWhile the specific details are unique, the general topic of flight disruptions due to operational issues is common, making the overall formulation somewhat generic.YesNoNoNoNoNoNoNoToo long, risk of truncation225NoDeclarative SimpleThe headline directly informs the reader about flight cancellations and delays, stating facts without employing rhetorical devices like questions or explicit calls to urgency.No[]No[]No[]No[]No[]No[]No[]Yes["Air France","American","Oman Air","Singapore","Qatar","Emirates","United","Heathrow","Schiphol","Charles de Gaulle"]NoYesThe headline highlights "Cancelled and Delayed" flights, "New Disruptions," and "Operational Challenges," which evoke concern for travelers and their plans.NoNoNoYesIndividuals with travel plans or connections to the mentioned airlines and airports will personally relate to the disruption and its potential impact.
296California Highway Patrol to launch statewide Maximum Enforcement Period this weekend to tackle issue of speeding2014724California Highway Patrol to launch statewide Maximum Enforcement Period this weekend to tackle issue of speedingNews_and_Current_EventsCrime & JudicialN/AHighThe main message is clear: California Highway Patrol will have a "Maximum Enforcement Period" to address speeding this weekend.HighTimely due to "this weekend" and relevant to drivers in California. Speeding is a persistent public safety concern.HighCalifornia Highway Patrol, statewide, Maximum Enforcement Period, speeding, this weekend are all highly relevant keywords.MediumWhile the specific details are new, announcements about enforcement periods are common. The phrase "tackle issue of speeding" is fairly generic.NoNoNoNoNoNoNoNoAdequate96Yes, 'Maximum Enforcement Period', justified useDeclarative SimpleThe headline makes a direct statement about an action the California Highway Patrol will take.Yes["this weekend"]No[]Yes["California Highway Patrol"]Yes["tackle"]No[]No[]Yes["California","statewide"]No[]NoYesThe headline directly addresses the 'issue of speeding' and announces a 'Maximum Enforcement Period,' which implicitly warns drivers of increased vigilance and potential consequences, triggering concern.NoNoNoYesThe headline refers to a 'statewide' enforcement, directly impacting anyone driving in California this weekend who might be concerned about speeding or encountering enforcement.
297250 million acres of public land to be sold off2002469250 million acres of public land to be sold offNews_and_Current_EventsEnvironmentN/AHighThe headline is very clear and directly states the core message without ambiguity.HighThe sale of public land is a topic of significant public interest and often involves current policy debates.High"public land" and "sold off" are highly relevant keywords. The large number "250 million acres" acts as a strong identifier.MediumWhile the topic of land sales is not new, the sheer scale of "250 million acres" makes this headline stand out.YesNoNoNoNoNoYesNoAdequate45NoDeclarative SimpleThe headline makes a direct and unambiguous statement about an event, without posing a question or using comparative language.No[]No[]No[]No[]No[]No[]No[]No[]NoYesThe phrase "sold off" combined with the immense scale ("250 million acres") of "public land" can evoke significant concern about loss or environmental impact.YesThe large number "250 million acres" is surprising and can evoke awe at the sheer scale of the proposed action.YesThe selling off of "public land" is often a controversial topic, and the headline is likely to provoke strong opinions and indignation.NoYesThe term "public land" directly connects to common ownership and collective interest, fostering personal identification in readers who feel a stake in such resources.
298Congratulations Pouring In For Legendary NFL Coach Tony Dungy1994857Congratulations Pouring In For Legendary NFL Coach Tony DungySportsFootballN/AHighThe main message is straightforward and easy to understand.HighNews about a prominent NFL figure like Tony Dungy is highly relevant to sports enthusiasts.HighUses strong keywords like "NFL Coach" and "Tony Dungy" relevant to the target audience.LowThe headline is quite generic for a congratulatory message and does not offer a unique angle.NoNoNoNoNoNoNoNoAdequate62NoDeclarative SimpleThe headline is a direct statement informing the reader about congratulations being received by Tony Dungy, without any rhetorical devices or specific structural classifications.No[]No[]No[]No[]No[]No[]Yes["NFL"]Yes["NFL","Tony Dungy"]NoNoNoNoYesThe phrase 'Congratulations Pouring In' evokes a positive and celebratory sentiment, aligning with hope/optimism.YesFans of Tony Dungy or the NFL might feel a sense of personal connection or pride regarding the news.
299TSA issues warning to not bring popular souvenir in your hand luggage – it'll be taken away if it's over a certain size1993759TSA issues warning to not bring popular souvenir in your hand luggage – it'll be taken away if it's over a certain sizePublic_SafetyAlerts & PreventionN/AHighThe message is clear and easy to understand: a specific item is warned against in hand luggage due to size restrictions.HighTravel regulations and TSA warnings are evergreen topics that are highly relevant to a broad audience, especially travelers.HighKeywords like "TSA", "warning", "souvenir", "hand luggage", and "taken away" are highly relevant and likely to attract the attention of those interested in travel news and regulations.MediumWhile "TSA warning" headlines are common, the specific mention of "popular souvenir" and the consequence of it being "taken away if it's over a certain size" adds a moderate level of differentiation.NoNoNoNoYesNoNoNoAdequate93NoDeclarative SimpleThe headline makes a clear and direct statement informing the reader about a warning issued by the TSA regarding certain items in hand luggage.No[]No[]Yes["TSA","issues warning"]No[]No[]Yes["not bring","taken away"]No[]Yes["TSA"]YesThe headline creates an information gap by referring to a "popular souvenir" without specifying what it is, prompting the reader to click to discover the item.YesThe phrases "not bring" and "it'll be taken away" evoke concern about potential loss of property or inconvenience during travel.NoNoNoYesThe use of "your hand luggage" and reference to a "popular souvenir" directly addresses and implicates the reader, making the warning personally relevant.